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

A Model Building Framework

In this brief chapter we present a framework within which we cast the task of data mining - the task being model building.

Model builders differ in how they represent the models they build (i.e., the discovered knowledge) and how they find the best model within this representation.

We can think of how a model builder represents the discovered knowledge as expressing sentences in a language, just as we express ourselves using sentences in our specific human languages (whether that be English, French, or Chinese, for example). As we know, there is an infinite number of sentences and paragraphs of sentences we can construct in our human languages.

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 representation of the model.

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