DiscussionThe Traveling Salesman Problem Essay Example | Topics and Well Written Essays - 250 words

DiscussionThe Traveling Salesman Problem - Essay Example In particular, the paper discusses how a business manager can effectively deliver a specific food product to major hotels located in the five cities. Transport problems, in business management, are considered as networks. A network, according to Shenoy et al (1989), is a set of nodes and arcs, where nodes are destinations and arcs are routes followed. In the problem, nodes are the cities, while arcs are the routes linking the cities. Apart from the nodes and arcs, a business manager considers capacity of a route, maximum flow of an entire system, and activity times in each path or route. In solving delivery problems, business managers use Critical Path Method (CPM) and Project Evaluation Review Technique (PERT). CPM uses the concept of critical activity and critical path in solving transport problems. A critical activity, in a network, is an activity whose time of start affects completion time of the entire project. In delivery problems, critical activities include loading and offloading, fueling and servicing, and drivers’ exchange times. In the delivery problem, critical activities also include time taken by a driver and /or a turn-boy to have breakfast, lunch, dinner, or supper. All these activities determine how fast or slow delivery to hotels in the five cities will be. Therefore, in CPM, a manager identifies points with these activities and organizes them such that minimum time and cost is spent in one complete flow of the food product (Shenoy et al., 1989). In PERT, direction of a flow is not fixed and is thus, considered as random variable. A probabilistic model is, therefore, used to identify a route with the shortest flow time. PERT considers activity time (t0), optimistic time (a), pessimistic time (b), and most likely time (m). Activity time measures duration of an activity, while optimistic time is the shortest possible time an activity can take. Pessimistic time is the longest possible time an activity

British Economy from November 2008 to november 2010 Coursework

British Economy from November 2008 to november 2010 - Coursework Example These measures were taken as a part of government’s austerity measures in the wake of rising government debt and the widening budget deficit. Some of the measures taken by the government included increase in Value Added Tax, systematic reduction in the general benefits provided to the people as well as reduction in the government expenditure besides taking other measures to ensure that the different macroeconomic objectives are fulfilled. George Osborne, man behind the recent measures taken by the UK government basically attempted to provide a radical program which can ensure that UK’s overall credit rating is improved amid the talks of country’s bankruptcy owing to mountains of debts which country accumulated over the period of time. In this part of the question, a discussion will be provided regarding the overall success of the measures taken by Bank of England and British Government since 2008 till date. Special emphasis will be on measuring and discussing the impact on growth, price stability, unemployment as well as the balance of payments. At the start of the financial crisis in later part of 2007, British Government, attempted to inject money into the system in order to ensure that the institutions do not fail and that the economy remains on the path of recovery after the decline. Bank of England (BoE) drastically reduced the base interest rate in order to stimulate the consumer spending and generate the required level of demand in the economy. During November 2008, Alistair Darling took radical measures to reduce the VAT however; the overall borrowing by the government was increased. Reduction in VAT was aimed at reducing the general price level and ensuring that the people spend so that employment can be generated and aggregate demand can be increased. However, the steps taken by Labor Government, from November 2008, also involved increasing the national insurance contribution as well as the increase in taxes for higher earning individuals.

Bruce Dawe Poem Analysis Essay Example for Free

Bruce Dawe Poem Analysis Essay Decode the question: How has the composer represented the concept of heroism in the film Gladiator? Make specific reference to two key scenes in the film. You MUST NOT choose scenes which have been analysed already in the course work (i.e. scenes I-III, XI-XIII, and XXVI). Write approximately 1000 words. Select Two key scenes: XXXI -Maximus has defeated the greatest gladiator ever to fight XLVI The Final Battle Create a mind map Organise ideas, texts references: Summary, presentation of Heroic concept, references to 2 keyed scenes with quotes notes Draft Do the draft as needed above Edit draft Edit as necessary Submit draft in Final written essay The Essay: Maximus was a powerful Roman General (strong belief, inner strength never dies, stood up for his belief no matter the consequences), loved by the people and the aging Emperor, Marcus Aurelius, known in history as the final of the Five Good Emperors. Before his death, the Emperor chose Maximus to be his heir over his own son, Commodus in an attempt to allow Rome to once more become a Republic. Then a power struggle left Maximus and his family condemned to death. The powerful General was unable to save his family, and his loss let him captured and put into slavery and trained as a Gladiator  by Proximo until he died. The only desire that fueled him at the time was the chance to rise to the top so that he would be able to look into the eyes of the man who would feel his revenge and fulfill the dying wish of his emperor. The time came when Proximos troupe was called to Rome to participate in a marathon of gladiator games held at the behest of the new emperor, Commodus. Once in Rome, Maximus wasted no time in making his presence known, and was soon involved in a plot to overthrow the emperor with his former-love Lucilla, Commodus sister, after whom he lusted, and also the widowed mother of Lucius, heir to the empire after his uncle, and democratic-minded senator, Gracchus was reinstated for Rome to republic. The composer has set up the representation of the heroism concept (50-60%) throughout the Gladiator film, from the opening scene, when the Hero leaves his pleasant vision (his wife and child) to return to the Germania battle field and to: face the 1st execution 1st fight as a gladiator release his name fight against the greatest warrior face Commodus fight in the Final battle As the opening scene and the above listed 3 scenes where the heroic concept has been discussed in the course work, the responder can also find the concept of heroism in the fight against the greatest gladiator and in the Final battle. The fight against the greatest gladiator Tigris turns to Caesars box, with swords crossed, he bows, We who are about to die salute you. Maximus stands by, showing no salute whatsoever. As Maximus gets ready to fight the large doors to the arena are thrown open, surprising Maximus. From each door emerges a team of men, who run into the arena. Each team picks up a chain from the sand. Maximus becomes distracted and Tigris kicks sand into Maximus face and begins the fight. As the fight continues and as Maximus is thrown to the ground, a trap door opens and out jumps a large tiger, pouncing at the fighting gladiators. Maximus is now  having to escape the claws of the tigers as he battles Tigris. In the background can be heard loose, loose, loose and pull, pull, pull as the handlers coordinate their efforts in handling each of the tigers that are now on the arena floor growling and charging at Maximus.] [Maximus manages to disarm Tigris, switching his sword from one hand to the other, Maximus stands ready to finish off Tigris. Suddenly, a fourth tiger jumps out of a trap door and jumps at Maximus. In that split second, Maximus turns and the tiger is speared with his sword. Maximus is thrown to the sand as the large beast lays atop him. Maximus stabs the beast repeatedly, killing it. The crowds cheer wildly. Tigris moves in for an attack. Maximus, on his back with the beast still on him, manages to grab Tigris own hatchet and with great force spikes him in the foot. Tigris bends over in pain, blood pouring from the opening of his mask. Maximus stands and kicks Tigris over to the ground. He is finished. The Final battle The trap door to the arena opens as the lift rises, encircled with the Praetorian as they stand behind their black shields. In the middle, the white clad Commodus, gazing upwardly, basking in the sun and the wounded, dying Maximus stand. All the while, Quintus staring at Maximus. As they reach the arena, the Praetorian take their place at the perimeter of the arena. Maximus stumbles to the center, slowly stooping to pick up a handful of sand, with a watchful eye on Quintus. Maximus rubs the sand in his hands and reaches for his sword but Quintus tosses it aside, and out of reach. Maximus painfully moves to where the sword has been tossed and picks it up, immediately swinging at Commodus. The two do battle. Maximus roars as he attacks Commodus. Commodus manages to cut Maximus leg. Although wounded, Maximus cuts Commodus arm causing him to drop his sword.] [Maximus begins to drift into the after-life and as he sees the gate to his home, the sword drops from his hand. Meanwhile, Commodus is calling Quintus for his sword but Quintus does not comply. Commodus then turns to the Praetorian, calling out sword. The guards begin to pull their swords when Quintus quickly tells them to sheath your swords and they quickly comply. As Commodus reaches for his hidden dagger, Maximus quickly returns to this life and, unarmed but for his strength and determination, does battle with Commodus, turning Commodus knife against him. Commodus tries relentlessly to fight  Maximus off but Maximus slowly plunges the knife into Commodus throat, further and further until it can go no further. Commodus falls to the ground. The fight over, Maximus begins to drift as he reaches out his bloodied hand, to push open the gate that leads to his home. Peace, once again, overcomes Maximus when Qu intus calls to him. Maximus, Maximus. Maximus regains consciousness.] To bring to life ancient Rome, director Ridley Scott employed great period costumes, chariots and horses, lots of dark-haired actors and actresses, and an amazing looking coliseum in the time period set in the movie. The musical score was possibly the best feature of this film, as the music is haunting and perfectly punctuates the dramatic action in soft, then ever-increasing tempos. It was a real trick to make music that would accentuate the flavor of this historical piece, and not distract it. The film used a very potent combination of long shots and close-ups to heighten the drama and yet propel responder into the immensity of the situation: the overwhelming doom that seems to be around any and every corner in Rome. For instance, at this scene where Maximus has defeated the greatest gladiator ever to fight, in a long, drawn-out battle. The bested warrior lies fallen at Maximus’ feet, awaiting his demise. The crowd chants Kill, Kill, Kill! The long shot shows Emperor Commodu s watching Maximus, and in the shot, the emperor is shown in the foreground, and Maximus appears small in the background. In this way the responder get a sense of the power that Commodus holds over Maximus. Now a medium-long shot of the crowd to get a sense of how many people are chanting for the vanquished opponent’s death; they all appear in unison, willfully they crave blood. Next a close-up of the emperor as he dramatically steps forward, arm extended. If he gives a thumbs up, the opponent should live. If he gives a thumbs-down, the opponent should be killed. His arm hangs outward and all eyes in the coliseum fall upon his hand. He gives the thumbs-down, and the crowd goes wild with roars of approval. Finally, a close-up of Maximus as he throws down his own sword in complete defiance to the emperor and to the people. The skillful editing helps contrast the morals Maximus and Commodus possess: Commodus is ruthless and political, Maximus does only what he has to, no more no less, and he is a man of his own conviction. This scene helps responder to understand why earlier the dying emperor Marcus Aurelius wanted Maximus to succeed him, he knew Maximus would do the right thing and not be  swayed easily by popular opinion. A leader must lead, not follow. Whenever the composer wanted the responder to get a sense of the size of this coliseum, he employed the cinematographer to use long range shots. When he wanted responder to get a sense of the hustle and bustle of Rome, a hand-held camera was used. The Final battle: The composer used a number of different techniques during the filming of the Final battle, including muted, washed out colors in the Coliseum. The scene is bright and colour comes across perfectly, setting the mood for the scene of battle. The fighting is filmed replete with quick cuts and a frenetic filming process; its disorienting but not so much that we cant keep track of whos who and what each participant is doing in said battle. The fighting is realistic without zooming in on the gore, as the violence speaks for itself. The composer correctly makes the scenes exciting without the need for a barf bucket.. Combat is supposed to be jarring and disorienting and the filming process actually communicates this sense to the responder very well. The responderd have to agree, as the scenes outstanding in special effects. They are seeing the Coliseum when it is a new(100 years old) and its grand structure, as it would have been in ancient Rome. Stunts are well coordinated and real tigers were used in a particular fight scene, grabbing out just inches from Maximus and his opponent. He earned his act on those days of shooting. Sound The soundtrack with its haunting score, is very memorable its scope and theme. Sound track manages to take a main character theme and vary it depending on the action, making it subtle and sweet at times, brooding and depressing at others. Throughout the film, the same musical elements are applied, bring unity and depth to the story. Besides digital sound, Dolby 5.1 is also included on the same disc

Data Stream Classification Of Red And White Wines Marketing Essay

Data Stream Classification Of Red And White Wines Marketing Essay Introduction Well we got 2 data sets to analysis using SPSS PASW 1) Wine Quality Data Set and 2) The Poker Hand Data Set. We can do this using CRISP methodology. Let us look what is CRISP by wikipedia CRISP-DM stands for Cross Industry Standard Process for Data Mining It is a data mining process model that describes commonly used approaches that expert data miners use to tackle problems. PASW Modeler is a data mining workbench that enables you to quickly develop predictive models using business expertise and deploy them into business operations to improve decision making. Designed around the industry-standard CRISP-DM model, IBM SPSS PASW Modeler supports the entire data mining process, from data to better business results. CRISP DM, Clementines own lightweight methodology of 5 stages Business Understanding, Data Understanding, Data Preparation Modelling, Evaluation and Deployment. CRISP Methodology Business Understanding: Understanding the project requirements objectives from a business perspective, and then converting this knowledge into a data mining problem definition Data understanding In this step following activities are going on, Data understanding, Collecting Initial Data then describing Data, Exploring Data and lastly verifying Data Quality The data preparation phase Tasks include table, record, and attribute selection as well as transformation and cleaning of data for modeling tools.Cleaning Data using appropriate cleaning and cleansing strategies then Integrating Data into a single point. Modeling: Selection and application of various modeling techniques done in this phase, and their parameters are adjusted to optimal values. Basically, there are more than one technique for the same data mining problem type. Some techniques have specific requirements on the form of data. Therefore, stepping back to the data preparation phase is often needed. Steps consist of Generating a Test Design, Building the Models assessing the Model Evaluation Building of model (or models) takes place in this phase. Before proceeding to final deployment of the model, it is important to more thoroughly evaluate the model, and review the steps executed to construct the model. Deployment In the final stage Knowledge gained is organized presented so that an end user can easily use it. As per the requirements this can be a report or a complex data mining process. Normally Customers carry out the deployment step Wine quality data set Wine quality is modeled under classification and regression approaches, which preserves the order of the grades. Explanatory knowledge is given in terms of a sensitivity analysis, which measures the response changes when a given input variable is varied through its domain The red wine data set contains 1600 samples out of which I have selected 200 random samples and doing the analysis(Data mining cannot discover patterns that may be present in the larger body of data if those patterns are not present in the sample being mined ) .So I selected the data set bearing in mind. The data set I have selected has high confidence. With measurements of 13 chemical constituents (e.g. alcohol, Mg) and the goal is to find the quality of red and white wine. Input variables 1 fixed acidity 2 volatile acidity 3 citric acid 4 residual sugar 5 chlorides 6 free sulphur dioxide 7 total sulfur dioxide 8 density 9 pH 10 sulphates 11 alcohol Output variable is quality (score between 0 and 10) CRISP methodology has been followed through out the phase .By checking the web site and resources learned about the wine domain .the next step was to check whether incorrect, missing or abnormal values in the data set end ensure the data quality. Data quality of the data set is very good. PASW Data stream classification of red and white wines Classification for Red and White wine 2 data sets red wine and white wine have been imported using variable file nodes Use of type node here is to describe the characteristics of data. . The Classification and Regression (CR) Tree node is a tree-based classification and prediction method. Similar to C5.0, this method uses recursive partitioning to split the training records into segments with similar output field values. The CR Tree node starts by examining the input fields to find the best split, measured by the reduction in an impurity index that results from the split. The split defines two subgroups, each of which is subsequently split into two more subgroups, and so on, until one of the stopping criteria is triggered. All splits are binary (only two subgroups) Red Wines variable importance White wine variable importance From variable importance diagram we can say that important attribute to determine Red wine quality is pH. The variable importance is in the order pH, citric acid, chloride as shown in the figure1. But for determining White wines quality the most contributing attribute is chloride and 2nd attribute is Alcohol. Decision Tree Model of white wine (only a portion) Analysis and conclusion The above generated tree consists of nodes and its children. The top node represent the total number of wine samples and how many number belongs to different categories(1 to 9).The first split is on chloride. This implies that most of the wine belongs to chloride level0.041.We see that good quality wine has chloride level It has been found from count Vs Quality graph that how many belongs to good quality categories. Alcoholic concentration of white wine samples is more than that of red wine sample. Good wines normally have high concentration. So we can conclude that White wine samples are good. In the white wine chloride level is normally high that implies it has got good Aroma. Where as in red wine the citric level is between particular levels that shows the red wine is very tasty!! PASW has got a number of 2-D and 3-D charts like bar, pie, histogram, scatter etc for time being I am using linear graph and 3-d scatter graph. You can use any of the graph as per the requirements. Some graphs are easy to interpret .Let us consider a 2-D graph between most contributing variable pH and quality from the graph it is clear that the relation ship between pH and quality is in such a way that if pH is in between 3.23 and 3.27 quality is good. Quality is very low for 3.38 and 3.50.We can plot similar graph between quality and citric acid or towards what ever contributing variable then find out the relation ship between them 2-D graph represent the relation ship between quality and pH of Red wine Let us plot a graph between chloride and Quality for the white wine. In the below figure it shows the quality is very good when chloride level below 0.036.And quality in the range 5 to 6 when chloride level is above .048. Like this if plot a graph between quality and alcohol we will see the quality is too good if alcoholic concentration in between 12.5 and 13(as per the sample I have analyzed) 3D graph which shows the relation ship between alcohol, quality and chloride level of white wine from the 2d analysis it was shown how the quality is being affected by single variable. If the one variable does not tell about how quality being related we can check relation ship between 3 variables using a 3d graph. It is having 3 axes. How Regression is useful In this multiple regression ,Predictors such as (Constant), alcohol, fixed acidity, residual sugar, chlorides, volatile acidity, free sulfur dioxide, sulphates, pH, total sulfur dioxide, citric acid, density determine the value of quality. Below gave a Pasw stream for regression. As per the variable importance graph volatile acidity, total SO2 and alcohol are most important variables in Regression analysis. Model R R Square Adjusted R Square Std. Error of the Estimate 1 .792(a) .626 .474 .542 Each by changing the independent variables value we can get value of dependent variable quality. With the help of a hypothesis we need to understand and build a relation ship among the variables. To predict the mean quality value for a given independent variable (say volatile acidity) we need a line which passes between the mean value of both quality and volatile acidity and which minimize the sum of distance between each of the points and predictive line. This fits into a line. The Poker Hand Data Set Each record is an example of a hand consisting of five playing cards drawn from a standard deck of 52. Each card is described using two attributes (suit and rank), for a total of 10 predictive attributes. There is one Class attribute that describes the Poker Hand. The order of cards is important and there are 480 possible Royal Flush hands. Below discussing about how to determine poker hands using data mining. I am considering classification only. If we consider clustering/Regression it does not make any sense PASW MODEL CLASSIFICATION USING CRT ALGORITHAM We got training and testing data set .First applying a model on training data set. Source file is a Comma separated file (CSV) with 1 million rows. It is difficult to do analyse on this input data set so selected sample data set and doing the analysis. Problem faced The given source data was not in a meaning full format so I have given meaningful attribute name and Values by using Vlookup function in MS excel, now the data has become more meaning full and it looks like below. Data cleansing is very important and comes under data preparation phase of the methodology Accuracy of predictive model The accuracy of predictive model is checked by analysis node. It has been found that accuracy is 90%. Using the Algorithm need to predict any of these: 0: Nothing in hand; 1: One pair;2: Two pairs;3: Three of a kind;4: Straight;5: Flush; 6: Full house;7: Four of a kind;8: Straight flush;9: Royal flush; Pocker hands variable importance diagram Let me say what did I understood from the diagram. Rank2 (rank of card2) is most contributing variable to predict poker hands. It is clear that Rank of 1st, 4th and 2nd cards are more contributing than suit of those cards. The different section of pie chart represents number of cards in a particular poker category. Blue represents No Poker; Red represents ONE PAIR, Green represent Royal flesh How Pasw helps to do classification Pasw has got number tree constructing algorithms(CR, c5.0) to do classification. I considered Classification and Regression (CR) though this is not a time efficient algorithm time complexity is more when compared to c5.0)I selected CR.The data set I have got is simple one and I am not considering the deep analysis all I need to do is to predict poker hands so CR can do it. Below shows the constructed tree using CR (Ashort description of tree already given above) Analysis Data has been classified into Training set and Testing set .Here most of the data set into a training set and small portion of data is used for testing.After a model has been processed by using the Training set, we can test the model by making predictions against the Test set. Since the data in the training set already contains known values for the attribute that you want to predict. Below giving the portion of training set being used. Integrating classification and association rule mining can produce more efficient and accurate classifiers.Here each row is an instance Trial: pair of 5 attributes (SUIT and RANK) + classification class. So this can be used to predict the classification of other unclassified instances. consider the training set given below suit1 rank1 suit2 rank2 suit3 rank3 suit4 rank4 suit5 Rank5 poker Heart ASS heart KING spades 4 Spades 3 heart QUEEN Nothing in hand Diamonds QUEEN diamonds 2 diamonds JACK Clubs 5 spades 5 ONE PAIR Hearts 10 hearts jack hearts king Hearts queen hearts 1 royal flesh Spades Jack spades king spades 10 Spades queen spades 1 royal flesh Diamond queen diamond jack diamond king diamond 10 diamond 1 royal flesh Hearts 5 diamond king spades king spades 7 clubs 5 two pairs Hearts 4 hearts 1 hearts 3 diamond 5 diamond 2 straight Suppose want to predict below hand is what type of Poker hand? suit1 rank1 suit2 rank2 suit3 rank3 suit4 rank4 suit5 rank5 poker Club 10 club jack club A club king club queen From the training set data the testing set is predicted, answer is Royal Flesh Conclusion Two data sets the wine and poker have been analysed using CRISP methodology and using the tool IBM SPSS PASW, used different modelling techniques which suits. Analysed the knowledge elicited by each model DATA MINING KNOWLEDGE DISCOVERY IN MARKETING (PART 2) Abstract Now-a-days Using the high power computing and information technology enables to collect store and process complex Marketing data. Data mining is used to extract knowledge from this marketing data. This report discuss about Data mining process, short discussion about different mining techniques such as classification tree, neural network, Regression and their application in marketing domain. My report Also cover different type of analyzes and tasks being used Introduction From the given topics I have selected the topic Data mining and Knowledge discovery for marketing since my cup of tea is Business and computing. I would always like to do research in Business analytics .Well let us look at what is data mining Data mining is the process of discovery of interesting, meaningful and actionable patterns hidden in large amounts of data . This is one of the tools to transform data into information. It is widely used in almost all fields of science and business profiling practice such as marketing, fraud detection, and scientific discovery. The technique to uncover pattern on data can also apply on sample data .so the sample data should be so the sample should be a good representative of larger data set. data mining can not find out the pattern which may be present in larger body of data and not contains in the small sub set of data. So this is very useful when sufficiently represented data are collected Most well known branches of data mining is knowledge discovery or KDD It derives knowledge from input data .This knowledge which have got from the process will become additional data and can be used for further discovery in related field normally an analyst can analysis and predict it.DM can generate thousands of pattern but all these patterns are not interested and useful. In this I am considering Data mining in a marketing field prospective. The data coming from different sources like transactions, loyalty cards, and discount coupons; customer complaint calls public life style studies using this data we can make Target marketing like to identify appropriate customer segments for new marketing initiatives determine customer purchasing pattern over time associations/co-relations between product sales, predict based on such association I mean cross market analysis what type of customer buys what type of product that is customer profiling Predict likelihood of customer churn and target those likely to leave with retention campaigns Customer requirement analysis like Identify the best products for different groups of customers and Predict what factors will attract new customers Provision of summary information such as Multidimensional summary reports and Statistical summary information (data central tendency and variation) Another question is why can not we go for a traditional data analysis instead of data mining? Answer is the field like marketing has tremendous Amount of data and it has multi dimension and complexity.A Marketing firm would likely to segment their customers into similar groups or clusters in order to better understand consumer behavior and more effectively market their products. In the past for a small business initiatives did not have trouble to understand their customers. They knew what they have to do once a customer approach them .Todays business is more competitive, more customer oriented, more products oriented so it is very difficult to understand the customer behavior, wants, needs the hidden relation ship between the data and preferences. With the help of data mining an analyst can deliver timely, personalized promotional offers. 1 Knowledge Discovery (KDD) Process S2 S1 S3 Data Cleaning Data Integration Databases Data Warehouse Knowledge Task-relevant Data Selection Data Mining Pattern Evaluation Normally in the huge DWH data mining environment data coming from various sources integrated and put it in data warehousing. Various data mining soft wares like teradata intelligent miners are used to mine Tera bytes of data and find market prediction. As I mentioned the DM is a Tools for developing predictive and descriptive models. Some are statistical method such as regression. Other use non statistical method like neural networks, classification trees. Here I considered some important tools then their How Classification trees are being used in marketing data mining Classification tree partition the data to maximize the difference in the dependent variable. it is also called a decision tree. Aim of classification tree is to classify the data into distinct groups or branches that create the strongest separation in the values of the dependent variables.The tree can identify segments. This can be helpful when a company is trying to understand what is driving market behavior. It detects nonlinear relationship. Mailed 10000 2.6% Male 4677 3.2% Female 2.15 2.1 % 1.7%  £30-45 3.6% > £45 4.1% Age>40 4.3% Age 0.7% Box shows resp rate in percentage The tree growth is through series of steps and rules .say for example sales pieces were mailed to 100000 names and yielded a response rate of 2.6%.the first split is on gender. This indicates that greatest difference between responders and non responders is gender. We see that males are much more responsive than females. We would consider males the better target group If we stop after one split. Our goal is to find out group with in both genders that discriminates between responders and non responders. In the next level split male and female groups are considered separately The second level split from the male node is on income, this implies that the income level varies in most between responders and non responders among the males. For female greatest difference is among the age group .It is very easy to identify the group with the highest response rate. Lets say that management decides to mail only to groups where the response rate is more than 3.5%.the offers would be directed to males who makes more than  £30000 a year and female over age 40 Some typical Classification tree Algorithms are 1) C4.5: Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann., 1993. 2) CART: L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, 1984 Linear regression and its applicability in marketing Knowledge of deviation from normal is very important for a marketer. In the past such deviations were very difficult to detect. Now-a-days data mining tools give great flexibility to detect and classify these changes. It is a statistical technique that quantifies the relationship between dependent variable and the independent variable, these are continuous. Consider the below equation, it shows a relation ship between sales and advertising along the regression equation .Our goal is to predict the sales based on the amount spend on advt. Plot a graph sales vs. advt that would be linear. A key measure of the strength of the relationship is the R-square. It measures the amount of overall variation in data that explained by the model. More than 70% Of the variation in sales can be explained by variation in advertising. Some times the relationship between sales and Advt is non linear (may be curvilinear) .By using the square root of advertising we are able to find better fit for the data. Sales=17.813+.0897*Advertising  £120  £1,503  £160  £1,755  £205  £2,971  £210  £1,682  £225  £3,497  £230  £1,998  £290  £4,598  £315  £2,937  £375  £3,622  £390  £4,402  £440  £3,844  £475  £4,470  £490  £5,492MINIMIZE SQUARED ERROR Advt sales ADVEGRTISIN -Æ’Â   x axis When building targeting models for marketing, risk and CRM, it is common to have much predictive variable. Using multiple predictive or independent continuous variables to predict a single continuous variable is called multiple linear regression .Targeting model created using linear regression is generally very robust. In marketing they can be used alone or in combination with other model. Neural Networks and its applicability in marketing Neural network does not follow any statistical distribution (Neural network is very vast topic a complete discussion is beyond the scope of this report) .it is modeled after the function of the human brain. The process is one of pattern recognition and error minimization. we can say it as nodes that are arranged in layers. The figure tells simple neural network with one hidden layer. Data has been classified into training and testing set (before the process).Then weight or input is assigned to each of the nodes in the first layer. During each iteration ,the input are processed through the system and compared to the actual value .the error is measured and fed back through the system to adjust the weights. The weights get better at predicting the actual results. A error limit is defined and it check with the error limit the process finishes when the minimum error limit reached One specific type of neural network commonly used in marketing uses sigmoidal functions to fit each node. This technique is very powerful in fitting a binary or twoilevel outcome such as response to an offer or a default on a loan Neural network not only pick linear data but also do a good pick up with non linear relation ship in the data. So this allows fitting data which is not possible to fit using regression. One disadvantage we can say that the result of neural net work is some what difficult to interpret A brief description on how Clustering can applicable in data mining Cluster analysis Cluster analysis group respondents with similar behaviors, preferences, or characteristics into segments. By doing so we can understand important similarities and differences between the respondents. Analyst can use this information to develop targeted marketing strategies, or to provide subgroups for analysis. In market survey data, clustering enables market researchers to group respondents who provide similar responses on several questions. In Clustering we use more than one variable that analyzes responses to several questions in order to find similar respondents. Clustering is based on the concept of creating groups based on their proximity to, or distance from, each other. Respondents within a cluster, therefore, are relatively homogenous. Most widely used Algorithms are 1)K-Means: MacQueen, J. B., Some methods for classification and analysis of multivariate observations, in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, 1967 2) BIRCH: Zhang, T., Ramakrishna, R., and Livny, M. 1996. BIRCH: an efficient data clustering method for very large databases. In SIGMOD 96 Let us look at some more major areas of application of data mining in the marketing like Customer profiling, Deviation analysis and Trend analysis. The pattern which formed after mining the data helps in analytics. Customer profiling This help to predict several marketing decision. A customer profile is a model of customer based on this marketer decides on the right strategies and tactics to meet the needs of that customer .The data mining task used in customer profiling can be dependency analysis, class identification and concept description. Below giving set of transaction that can help marketer to construct useful customer profiles. Frequency of purchases Marketing firm can build targeted promotion offer such as frequent buyer programs by looking how often their customer purchases product from their shop. Rcency of purchases The meaning of term is How long has it been since this customer last placed an order? Suppose a customer frequently visit the shop.It has been found that the specific customer or customer group not visiting the firm over long period of time .Market investigate the reason. By knowing this they can take appropriate offer or action. Size of purchases It tells, on a particular transaction how much he or she spends. This information helps to give resources to those customer groups. Identifying typical customer groups It gives characteristics of each group .For example a profile indicating that the customer has purchased a WINDOWS 7 SOFTWARE CD may hold to the marketer offering a special deal for MICROSOFT OFFICE SOFTWARE CD. Prospecting Customer profiles like buying patterns, give clues to the marketer on prospective customers. Say for example, consider the pattern Purchase of Norton Anti Virus package with one year validity is followed by purchase of Norton Up gradation version /or new version within 11 months about 85% of the time by high income customers discovered by data mining. Analyst who analysis pattern can identify the prospective customers for Upgraded/new version based on first time purchase details and tailor the mail catalog accordingly, thus, increasing the prospect of sales. 2 Deviation analysis Deviation analysis is one of the important analysis for example a higher than normal credit purchase on a credit card can be a fraud anomaly or a genuine purchase by the customer changes.Once a deviation has been discovered as a fraud, the marketer takes appropriate steps to prevent such frauds and initiates corrective action.If the deviation has been discovered as a change, further information collection is necessary. For example, a change can be that a customer got a new job and moved to a new house. In this case, the marketer has to update the knowledge about the customer. 3) Trend analysis Trends are patterns that persist over a period of time. Trends could be short-term trends like the immediate increase and subsequent slow decrease of sales following a sales campaign. Or, trends could be long-term, like the slow flattening of sales of a product over a few years. Data mining tools, such as visualization, help us detect trends, sometimes very subtle and hidden in the database, which would have been missed using traditional analysis tools like scatter plots. In marketing decisions, trends can be used for evaluating marketing programs or to forecast future sales. Data mining task in marketing data mining domain These tasks present in all data mining process we are just looking it into marketing prospective Dependency analysis Data Visualization Class identification Deviation Detection Concept Description Dependency analysis The market basket analysis gives the relationship between different product purchased by a customer .Using this techniques we can develop marketing strategy for promoting product that have dependency relationship in customers mind. Class identification It groups customers into classes which are defined in advance. Mathematical taxonomy and clustering are being used for class identification task. What the first one does is it maximizes the similarity with in classes but minimize similarity between classes. In clustering approach it determine the clustering according to attribute similarity as well as conceptual cohesiveness as defined by domain knowledge (describe above). A company doing business over the net, based on the session log data of internet users, the firm can classify the web users into email only users Surfers or Just for fun Surfer etc Concept description Comparison analysis will be done using statistical techniques. Using this we can compare marketing and customer knowledge. Deviation detection This helps us to determine the anomaly and changes. We can find the anomaly from various statistical techniques. This is already being explained above. Data visualization This kind of softwares allows the market research team or concerned people to view complex 3-D and 2-D patterns. They also provide drill down drill up slice facilities. In the KDD (knowledge discovery from data base) process, data visualization is used in association with other tasks such as dependency analysis, class identification, deviation detection and clustering. IBM SPSS PASW has got good data visualization techniques. Some of them are explained in Part 1 of the report. Conclusion Report discussed about Data mining process,

The Writing Process :: essays research papers

The Six Stages of the Writing Process 1. Planning:  Ã‚  Ã‚  Ã‚  Ã‚  Planning is the process of setting document objectives, analyzing audience needs and responses, and developing a course of action to accomplish the objectives. Effective planning takes time at the beginning of the project, but overall saves a lot of time. 2. Research:  Ã‚  Ã‚  Ã‚  Ã‚  Research is the systematic investigation of a subject in order to discover facts, opinions, or beliefs. The amount of research needed for a written assignment depends on the nature of the document and the information available about the subject. While minimal research is usually needed for simple memos or letters, longer, more complex documents may require more. 3. Organization:  Ã‚  Ã‚  Ã‚  Ã‚  Organization relates to the decisions writers make based on their communication objectives, audience requirements, and format limitations. These decisions determine the order, in which they present their ideas, and logical connections that exist among these ideas, and the approach they take to present the ideas. 4. Composition:  Ã‚  Ã‚  Ã‚  Ã‚  This process involves following your organizational writing plan to produce a rough draft. As this process begins writers make decisions about such matters as tone, style, and level of formality. 5. Design:  Ã‚  Ã‚  Ã‚  Ã‚   Design is the process of placing information on a page so that it is easily read. Various design elements help clarify organization, including headings, underlining, and bulleted lists. 6. Revision:  Ã‚  Ã‚  Ã‚  Ã‚  This is the final stage of the writing process. It includes five specific steps that transform a rough draft into a finished document. These steps include the following:   Ã‚  Ã‚  Ã‚  Ã‚  Ensure the best words, style, and tone are used.   Ã‚  Ã‚  Ã‚  Ã‚  Check for clarity and conciseness and remove all jargon.   Ã‚  Ã‚  Ã‚  Ã‚  Eliminate all punctuation, grammatical and spelling errors   Ã‚  Ã‚  Ã‚  Ã‚  Focus on coherence through the use of effective transitions.   Ã‚  Ã‚  Ã‚  Ã‚  Check for factual errors. The Five Steps in the Writing Process 1. Purpose:  Ã‚  Ã‚  Ã‚  Ã‚  You have to understand your aim or intention for writing. You must know if you are writing to inform, to persuade, to describe, to narrate, to summarize, to define, or to compare. 2. Audience:  Ã‚  Ã‚  Ã‚  Ã‚  You have to know your audience and how that audience might influence your approach. 3. Stance:  Ã‚  Ã‚  Ã‚  Ã‚  Stance refers to the combined effect of voice and tone. Voice is your relationship with the audience and tone is the relationship with your subject. 4. Research:  Ã‚  Ã‚  Ã‚  Ã‚  During this step one has to decide if research needs to be conducted or whether your current information is adequate. 5. Design:  Ã‚  Ã‚  Ã‚  Ã‚  Design refers to a clear sequence for communicating your information most effectively. Helping to Achieve the Writing Objective The thesis is your basic position and is usually conveyed in a single sentence.

Vandy Religion Essay :: essays papers

Vandy Religion Essay I have been around religion all my life, but only recently has it become important to me. I find this ironic, because I recently transferred to a ‘public’ DODDS school from a small, private Catholic school. Only after I was removed from the Catholic school environment did I begin to see what religion really is. I came to the Catholic school system in Leavenworth, Kansas in the fifth grade. Before that time, I was naà ¯ve; I barely knew any swear words and all I knew about sex was that I was interested in cute boys. Ironically, it was this time, all through and every year after fifth grade that I was subjected to and learned vulgarity. By the time I was a sophomore, I was so familiar with all of the four-letter words that I swore casually with my friends when we would discuss sex or gossip about other students. I picked up every slang word for every body part and knew every sexual innuendo there was to know, all during the six years I attended Catholic school. Even though we were not very good examples of church-going youngsters, my friends and I regularly went to Youth Group meetings. I never really listened to what the speaker was saying or to what I was singing, I just went to socialize with my friends. When we moved to Okinawa, I immediately wanted to make friends with the popular crowd, but my mother forced me to go to Youth Group. I only knew one person there, but I met another girl, who introduced me to her group of friends, the exact crowd I wanted to meet. We went out a couple of times, but after a few weeks at school, they decided I was not ‘cool enough’ and dropped me from their group. I was crushed. However, since I was new, I had met other new people, who I brought to the Youth Group so I would not be alone. Soon I met other people at the Youth Group and began to get more involved with that crowd. They did not reject me because of my appearance or because I sometimes said stupid things, but accepted me for who I was and welcomed me into their group.

Role of Kamala in Hermann Hesse’s Siddhartha

The novel Siddhartha written by Hermann Hesse is a philosophical novel that explores the journey of life and to enlightenment. This is done through the narration of the life of a young boy – the eponymous Siddhartha by a third-person omniscient narrator. My goal in this essay is to explore the role of the most important female character in Siddhartha, Kamala. Siddhartha is set in India, the story concurs with the life of Gotama the Buddha and therefore is estimated to take place around the 5th-6th century B. C. Many female characters play a part in Siddhartha’s journey. Siddhartha’s mother, the nameless young woman in the forest that attempts to seduce him and Vasudeva’s deceased wife. However the only female character that plays a significant role in the plot is Kamala, a courtesan who meets Siddhartha outside the city and becomes an influential character. The root word of the name Kamala – â€Å"Kama† is the Hindu god of love and desire; this represents her profession and character. Kamala first appeared in the eponymous chapter. Siddhartha meets Kamala outside the city when she was being escorted by her servants. Immediately, Siddhartha is struck by her beauty and decides to find her in the city. He saw beneath high-piled black hair a very fair, very soft, very clever face, bright-red lips like a newly opened fig, eyebrows well tended and painted in the form of high arches, dark eyes clever and alert. † The immediate circumstances in which we meet Kamala give us the impression of her being a very beautiful and rich, yet mysterious and untouchable given Siddhartha’s social and financial situation. He then enters the city and asks for her name, Siddhartha learns that she is the renowned courtesan Kamala, who is wealthy and owns a house in the city. His decision to visit Kamala brings about a turning point in the plot where Kamala becomes an object of desire for Siddhartha, and also he views her as someone capable of tutoring him in the ways of love. However Kamala initially rejects Siddhartha as he has no possessions and wears ragged clothing. She does however; give Siddhartha a kiss for a poem he performs. â€Å"He lowered his face to hers, and placed his lips on those lips that were like a newly opened fig. † She introduces Siddhartha to Kamaswami, who is a merchant and a regular client of Kamala’s. She tells Siddhartha to work with Kamaswami and learn the way of the merchant in order to earn money for himself. This becomes important as Siddhartha does become a successful merchant like Kamaswami changing him into a respected wealthy man. Eventually Kamala accepts him and shows him the world of physical love and sex. â€Å"[Siddhartha] learned the art of love; he practiced the cult of pleasure, in which more than anywhere else giving and taking become one and the same; he chatted with her, learned from her, gave her advice, received advice. This persists for many years as Kamala continues her relationship with Siddhartha, but comes to an end when the latter becomes disillusioned with the material world and runs away from the city. Only after Siddhartha leaves the city does Kamala find that she is pregnant with his child and decides to accept no other lovers, the story then leaves Kamala. Kamala returns later when she and her son are on their way to see the dying Buddha Gotama. By this time Siddhartha has returned to his old ascetic lifestyle living with the ferryman Vasudeva. Whilst resting by the river Kamala is bitten by a poisonous snake, Vasudeva hears her son calling for help and immediately goes to assist. Vasudeva brings Kamala back to the hut where Siddhartha recognizes her, and realizes that the boy is his son. Kamala lives only long enough to have one last conversation with Siddhartha before she dies in his arms (The Ferryman chapter). We see Kamala as a temptress who seduces Siddhartha and draws him away from his journey to enlightenment. She does however indirectly lead Siddhartha to his enlightenment first by teaching him the values and limitations of the material world, and also by bearing his son who gives Siddhartha the most difficult test on his path. Kamala is the master tutor of the material world, this makes her the opposite of Gotama who is the master tutor of the spiritual world. Whilst Gotama teaches his followers the virtues of patience and inner peace, Kamala focuses on a lifestyle of â€Å"living in the moment†. She also contrasts the Samanas whom Siddhartha has become when he first meets Kamala. The Samanas live without personal property but Kamala demands items such as clothing and jewelry from clients for her courtship. While we see Kamala’s relationship with Siddhartha as mutual love, the two never truly love each other. Siddhartha only sees Kamala as a teacher of love and an object of desire, Kamala sees Siddhartha as a skilled lover, a client and a source of income (she does however show preference and affection for Siddhartha, as we see in the initial chapters when she gives Siddhartha the opportunity to earn a living in the city). For a long while she sported with Siddhartha, luring him on, repulsing him, forcing his will, encircling him, enjoying his mastery, until he was vanquished and lay exhausted at her side. † She treats her relationship with Siddhartha as a part of her profession. However, after Siddhartha leaves and Kamala becomes aware of her pregnancy, she refuses to take another lover. This tells us that she still had a very intimate relationship with Siddhartha. An interesting fact is that despite Siddhartha’s dislike for teachers (as shown in his conversations with Gotama the Buddha and later with Govinda), he shows a preference towards Kamala’s teachings. Towards the very end of her life, Kamala seems to have also found an inner peace. Kamala is described as physically very beautiful and alluring, whilst at the same time being very clever (although we do learn that she cannot read nor write). â€Å"Her body was as lithe as a jaguar’s or as a hunter’s bow. † Kamala’s most important role in Siddhartha is being Siddhartha’s mentor in the world of love and as the mother of Siddhartha’s child. She plays a major role in Siddhartha’s life as a long-time companion and a lover of sorts. Initially we see her as an obstacle to Siddharta’s journey to find enlightenment as she seduces Siddhartha and keeps him from his continuing on his trek, but soon we see that while Siddhartha has experienced much of the spiritual world, he lacks any experience in the material world and is naive to the concepts of love. Here is where we begin to see her as an instructor, a companion and a guide to Siddhartha’s other side of life (the original being a life of ascetics). Reference http://www.123helpme.com/view.asp?id=169147