DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Example

You are sitting with the finance manage of the car sales yard applying, on-line, for a loan to purchase a new people mover. The finance manager is asking you a series of questions. You think it odd that just a little while ago you overheard the same finance manager using the same on-line system, asking someone else different questions, but not to worry, your circumstances are different. You supply the details requested and walk out with a new set of wheels.

Over the coming year as you pay back the loan with monthly repayments the financial institution who actually lent you the money is monitoring your account. Are you paying back on time? Or are you sometimes late? Or do you never pay unless you are visited by the debt collector? In the end, were you a good or a bad customer for the financial institution?

The financial institution can use their experience with you and many other customers to develop a prediction system to indicate for each new customer their likelihood of being a good or bad customer. It is this system that decides which are the more important variables to take into account in making that decision. For some combination of variables more details and more questions, and different questions need to be answered.

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