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Book Synopsis
Introduction to knowledge discovery in databases.- Part I Preprocessing methods.- Data cleansing.- Handling missing attribute values.- Geometric methods for feature extraction and dimensional reduction.- Dimension Reduction and feature selection.- Discretization methods.- outlier detection.- Part II Supervised methods.- Introduction to supervised methods.- Decision trees.- Bayesian networks.- Data mining within a regression framework.- Support vector machines.- Rule induction.- Part III Unsupervised methods.- Visualization and data mining for high dimensional datasets.- Clustering methods.- Association rules.- Frequent set mining.- Constraint-based data mining.- Link analysis.- Part IV Soft computing methods.- Evolutionary algorithms for data mining.- Reinforcement-learning: an overview from a data mining perspective.- Neural networks.- On the use of fuzzy logic in data mining.- Granular computing and rough sets.- Part V Supporting methods.- Statistical methods for data mining.- Logics

Data Mining and Knowledge Discovery Handbook

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    A Hardback by Oded Maimon

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      Book details

      Publisher Springer
      Published 10 January 2010
      ISBN-13 9780387098227
      978-0387098227
      ISBN-10 0387098224

      Description

      Book Synopsis
      Introduction to knowledge discovery in databases.- Part I Preprocessing methods.- Data cleansing.- Handling missing attribute values.- Geometric methods for feature extraction and dimensional reduction.- Dimension Reduction and feature selection.- Discretization methods.- outlier detection.- Part II Supervised methods.- Introduction to supervised methods.- Decision trees.- Bayesian networks.- Data mining within a regression framework.- Support vector machines.- Rule induction.- Part III Unsupervised methods.- Visualization and data mining for high dimensional datasets.- Clustering methods.- Association rules.- Frequent set mining.- Constraint-based data mining.- Link analysis.- Part IV Soft computing methods.- Evolutionary algorithms for data mining.- Reinforcement-learning: an overview from a data mining perspective.- Neural networks.- On the use of fuzzy logic in data mining.- Granular computing and rough sets.- Part V Supporting methods.- Statistical methods for data mining.- Logics

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