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

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. I am also in debt to my colleagues over the years, particularly Peter Milne and Zhexue Huang, for their support and contributions over the years in the development of Data Mining in Australia.

Colleagues in various organisations deploying or developing skills in data mining have also provided significant feedback, as well as the motivation, for this book. These include, but are not limited to my Australian Taxation Office colleagues, Stuart Hamilton, Frank Lu, Anthony Nolan, Peter Ricci, Shawn Wicks, and Robert Williams.

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.

Many have contributed to the content of the book, providing insights and comments. Illustrative examples of using R have also come from the R mailing lists and I have used many of these to guide the kinds of examples that are included in the book. Many contributors to that list need to be thanked, and include Earl F. Glynn.

Financial support for maintenance of the book is always welcome. Financial support is used to contribute toward the costs of running the web pages and the desktop machine used to make this book available.

I acknowledge the support of many, including: Milton Cabral, Gail McEwen, Wade Thunborg, Longbing Cao, Martin Schultz, Danilo Cillario, Toyota Finance, Michael Stigall, Melanie Hilario, Siva Ganesh, Myra O'Regan, Stephen Zelle, Welling Howell, Adam Weisberg, Takaharu Araki, Caroline Rodriguez, Patrick L Durusau, Menno Bot, Dorene A Gilyard, Henry Walker, Fei Huang, Di Peter Kralicek, Lo Siu Keung, Julian Leslie, Mona Habib, John Chow, Michael Provost, Hamish R Hutchison, Chris Raymond, Keith Lyons, Shawn Swart, Hubert Weikert, and Tom Thomas.

Graham J Williams, Canberra

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