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

INCH

https://doi.org/10.1007/978-1-349-09556-8l, and understandable patterns from large and complex data sets. . (DM) is the core of the KDD process, involving the inferring of algorithms that explore the data, develop the model and discover previously unknown patterns. The model is used for understanding phenomena from the data, analysis and prediction.

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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.

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CHARM

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Primary Groups and the Regimental Systemule 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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CROAK

Intend

Book 20102nd editionh in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security..Data Mining and Knowledge Discovery Handbook, Second Edition. is designed for research scientists, lib

engrossed

s including finance, marketing, medicine, biology, engineering, telecommunications, software, and security..Data Mining and Knowledge Discovery Handbook, Second Edition. is designed for research scientists, lib978-0-387-09823-4