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

Summary

Market:	General data mining applications.
Techniques:	Association analysis using apriori. Classification using C5.0. Clustering using k-means (See Chapter ) and Kohonen self organising maps. Multivariate using principle component analysis and factor analysis. Regression using neural networks. Visualisation (Web Graph).
Platforms:	Linux, Unix, MSWindows.
Website:	http://www.spss.com/spssbi/clementine/
Vendor:	SPSS, Chicago, Illinois.

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