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

Borgelt Data Mining Suite:
From University of Magdeburg

Christian Borgelt of the Working Group on Neural Networks and Fuzzy Systems, Otto-von-Guericke-University of Magdeburg, Germany, makes available a collection of data mining algorithms. These are licensed under the GNU General Public License and available freely for download from the Internet for both Unix/Linux and for MSWindows. Both binary versions and source code are available. This is a tremendous resource for anyone getting started in data mining and indeed provide a suite of tools that are as good as, if not better than, many commercial offerings. Indeed, the apriori algorithm implemented by Borgelt is considered to be one of the fastest available and is also available as part of the Clemetine package from SPSS (See Chapter ).

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