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

Moving into R

R is a statistical programming language together with an extensive collection of packages (of which Rattle is one) that provide a comprehensive toolkit for, amongst other things, data mining. Indeed, R is extensively deployed in bio-informatics, epidemiology, geophysics, agriculture and crop science, ecology, oceanography, fisheries, risk analysis, process engineering, pharmaceutical research, customer analytics, sociology, political science, psychology, and more. It is a relatively simple language to learn.

Support further development through the purchase of the PDF version of the book.
Brought to you by Togaware.