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

Acknowledgements

Many thanks to my many students from the Australian National University over the years who have been the reason for me to collect my thoughts and experiences with data mining to bring together into this book. I have benefited from their insights into how they learn best. They have also contributed in a number of ways with suggestions and example applications.

Colleagues in various organisations deploying or developing skills in data mining have also provided significant feedback, as well as the motivation, for this book.

This book has grown from a desire to share experiences in using and deploying data mining tools and techniques. A considerable proportion of the material draws on over ten years of teaching data mining to undergraduate and graduate students and running industry outreach courses. The aim is to provide recipe type material that can be instantly deployed, as well as reference material covering the concepts and terminology a data miner is likely to come across.

Graham J Williams, Canberra

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