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

Preface

Knowledge leads to wisdom and better understanding. Data mining builds knowledge from information, adding value to the tremendous stores of data that abound today--stores that are ever increasing in size and availability. Emerging from the database community in the late 1980's the discipline of data mining grew quickly to encompass researchers and technologies from Machine Learning, High Performance Computing, Visualisation, and Statistics, recognising the growing opportunity to add value to data. Today, this multi-disciplinary effort continues to deliver new techniques and tools for the analysis of very large collections of data. Searching through databases measuring in the gigabytes and terabytes, data mining delivers discoveries that improve the way an organisation does business. It can enable companies to remain competitive in this modern data rich, knowledge hungry, wisdom scarce world. Data mining delivers knowledge to drive the getting of wisdom.

The range of techniques and algorithms used in data mining may appear daunting and overwhelming. In performing data mining for a data rich client many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of application.

In this book we deploy the Free and Open Source Software package Rattle, which is built on top of the R system to illustrate the deployment of data mining technology. As Free Software the source code of Rattle and R is available to anyone, and anyone is permitted, and indeed encouraged, to extend the software, and to read the source code to learn from it. Indeed, R is supported by a world wide network of some of the world's leading Statisticians.

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