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

Text Mining with R

A common procedure for text mining is to `score' each document by a vector that records the frequency of occurrence of commonly used and subject matter specific words and phrases. Assuming the documents are themselves classified into a number of classes already (perhaps those that are relevant versus those that are not) you can use this ``training set'' with any of the many supervised learning or classification tools in R (e.g., trees, logistic regression, boosting, Random Forests, support vector machines, linear discriminant analysis, etc.).

See ttda

Text mining begins with feature extraction. Techniques include:

Keyword extraction

Bag of words

Term weighting

Co-occurrence of words

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