DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Introduction Data Mining with Rattle Data Exploring Data Transforming Data Cluster Analysis Association Analysis Classification and Regression Models Evaluation and Deployment Issues Moving into R Troubleshooting Beyond Rattle R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines 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 Installing GTK, R, and Rattle Glossary Bibliography Index

Building Models

In this section we present a framework within which we cast the task of data mining--the task being model building. We refer to an algorithm for building a model as a model builder. Rattle supports a number of model builders, including decision tree induction, boosted decision trees, random forests, support vector machines, logistic regression, kmeans, and association rules. In essence, the model builders differ in how they represent the models they build (i.e., the discovered knowledge) and how they find (or search for) the best model within this representation.

We can think of the discovered knowledge, or the model, as being expressed as sentences in a language. We are familiar with the fact that we express ourselves using sentences in our own specific human languages (whether that be English, French, or Chinese, for example). As we know, there is an infinite number of sentences that we can construct in our human languages.

The situation is similar for the ``sentences'' we construct through using model builders--there is generally an infinite number possible sentences. In human language we are generally very well skilled at choosing sentences from this infinite number of possibilities to best represent what we would like to communicate. And so it is with model building. The skill is to express within the language chosen the best sentences that capture what it is we are attempting to model.

We formally present this general framework. The following sections then present models builders for various tasks in the context of this framework.

FRAMEWORK GOES HERE

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