Titlebook: Data Mining and Knowledge Discovery Handbook; Oded Maimon,Lior Rokach Book 20102nd edition Springer Science+Business Media, LLC 2010 Bayes

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Rule Inductionule induction methods: LEM1, LEM2, and AQ are presented. An idea of a classification system, where rule sets are utilized to classify new cases, is introduced. Methods to evaluate an error rate associated with classification of unseen cases using the rule set are described. Finally, some more advanced methods are listed.

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Frequent Set MiningFrequent sets lie at the basis of many Data Mining algorithms. As a result, hundreds of algorithms have been proposed in order to solve the frequent set mining problem. In this chapter, we attempt to survey the most successful algorithms and techniques that try to solve this problem efficiently.

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HPV Detection and Clinical Implicationsrning from qualitative data. Discretization addresses this issue by transforming quantitative data into qualitative data. This chapter presents a comprehensive introduction to discretization. It clarifies the definition of discretization. It provides a taxonomy of discretization methods together wit

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Mike Anderson: The Kid Who Wanted to Flynivariate vs. multivariate techniques and parametric vs. nonparametric procedures. In presence of outliers, special attention should be taken to assure the robustness of the used estimators. Outlier detection for Data Mining is often based on distance measures, clustering and spatial methods.