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

Weka:
From University of Waikato

WEKA (the Waikato Environment for Knowledge Analysis) is an open source and freely available workbench for applying machine learning techniques to practical problems, integrating many different machine learning tools within a common framework and a uniform, if basic but functional, user interface. WEKA incorporates over 60 machine learning techniques, ranging from traditional decision trees, association rules, clustering, through to modern random forests and support vector machines. A WEKA user is able to use machine learning techniques to derive useful knowledge from quite large databases. Typical users include both researchers and industrial scientists.

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