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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Support Vector Machinesclassifiers creates a maximum-margin hyperplane that lies in a transformed input space and splits the example classes, while maximizing the distance to the nearest cleanly split examples. The parameters of the solution hyperplane are derived from a quadratic programming optimization problem. Here, w

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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 advanc

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Outlier Detectionnivariate 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.

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Supervised Learningsed in subsequent chapters. It presents basic definitions and arguments from the supervised machine learning literature and considers various issues, such as performance evaluation techniques and challenges for data mining tasks.

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Bayesian Networksntal aspects of Bayesian networks and some of their technical aspects, with a particular emphasis on the methods to induce Bayesian networks from different types of data. Basic notions are illustrated through the detailed descriptions of two Bayesian network applications: one to survey data and one to marketing data.

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Data Mining within a Regression Frameworkedures are discussed within a regression framework. These include non-parametric smoothers, classification and regression trees, bagging, and random forests. In each case, the goal is to characterize one or more of the distributional features of a response conditional on a set of predictors.