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

Resources and Further Reading

The example data for contact lenses comes from cendrowska:1987:prism and is available from the machine learning repository at ftp://ftp.ics.uci.edu/pub/machine-learning-databases/lenses/lenses.data.

A problem with naïve Bayes arises when the training database has no examples of a particular value of a variable for a particular class.

Bayesian networks relax the conditional independence assumption by identifying conditional independence among subsets of variables.

kohavi:1996:scaling_bayes addressed the problem of independence by combining naïve Bayes with decision trees. The decision tree is used to partition a database and for each resulting partition (corresponding to separate paths through the decision tree) a naïve Bayes classifier is built using variables not included in the corresponding path through the decision tree. Whilst some improvement in accuracy can result, the final knowledge structures tend to be less compact (with replicated structures). Nonetheless this may be a useful approach for very large databases.

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