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

Building Models

Modelling is what people often think of when they think of data mining. Modelling is the process of taking some data (usually) and building a model that reflects that data. Usually the aim is to address a specific problem through modelling the world in some way and from the model develop a better understanding of the world.

There is a bewildering array of tools and techniques at the disposal of the data miner. We can get a better understanding of what is available through categorising the algorithms according to the types of analysis performed. In this chapter we introduce and summarise the broader categories of data mining analysis. Part  then presents, in a systematic manner, many algorithms that are used in data mining and available either freely or else implemented in commercial toolkits.

Much of the terminology used in data mining has grown out of that used in both machine learning and statistics. We identify, for example, two very broad categories of analysis as unsupervised and supervised (as in supervised and unsupervised learning).

We introduce such an ordering to the world of data mining techniques in this chapter. In summary:

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