DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface List of Figures List of Tables Process . Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Conditional Trees Decision Trees Hierarchical Clustering Kernel Methods K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products Borgelt Data Mining Suite R Rattle Weka Closed Products C4.5 Clementine Enterprise Miner Equbits Foresight GhostMiner InductionEngine ODM Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Organisation

Part I introduces the basic concepts and processes in data mining, with a focus on preparing for modelling within data mining. Here we deal with the concepts that will be found in data mining, providing a solid grounding for understanding the many issues and ideas around data mining, serving as the foundation for that which follows.

Part II reviews the algorithms employed in data mining. The encyclopedic overview covers many tools and techniques deployed within data mining, ranging from decision tree induction and association rules, to multivariate adaptive regression splines and patient rule induction methods. We also cover standards for sharing data and models.

We continue the Desktop Guide with a snapshot of some current alternative open source and then commercial data mining products in Parts III and IV: Open Source Products and Commercial Off The Shelf Products.

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