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

Goals

This book presents a unique and easily accessible single stop resource for the data miner. It provides a practical guide to actually doing data mining. It is accessible to the information technology worker, the software engineer, and the data analyst. It also serves well as a textbook for an applications and techniques oriented course on data mining. While much data analysis and modelling relies on a foundation of statistics, the aim here is to not lose the reader in the statistical details. This presents a challenge! At times the presentation will leave the statistically sophisticated wanting a more solid treatment. In these cases the reader is referred to the excellent statistical expositions in dalgaard:02:introd_statis_r, venables.ripley:2002:modern_app_stats, and hastie.tibshirani.etal:2001:stats_learn.

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