Foundations of Data Mining and knowledge Discovery

ISBN: 978-3-540-26257-2 (Print) 978-3-540-32408-9 (Online)

Table of contents (21 chapters)

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  1. No Access

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    Pages 1-19

    Knowledge Discovery as Translation

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    Pages 21-42

    Mathematical Foundation of Association Rules – Mining Associations by Solving Integral Linear Inequalities

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    Pages 43-70

    Comparative Study of Sequential Pattern Mining Models

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    Pages 71-86

    Designing Robust Regression Models

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    Pages 87-100

    A Probabilistic Logic-based Framework for Characterizing Knowledge Discovery in Databases

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    Pages 101-117

    A Careful Look at the Use of Statistical Methodology in Data Mining

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    Pages 119-130

    Justification and Hypothesis Selection in Data Mining

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    Pages 131-141

    On Statistical Independence in a Contingency Table

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    Pages 143-160

    A Comparative Investigation on Model Selection in Binary Factor Analysis

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    Pages 161-170

    Extraction of Generalized Rules with Automated Attribute Abstraction

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    Pages 171-184

    Decision Making Based on Hybrid of Multi-Knowledge and Naïve Bayes Classifier

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    Pages 185-210

    First-Order Logic Based Formalism for Temporal Data Mining*

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    Pages 211-231

    An Alternative Approach to Mining Association Rules

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    Pages 233-264

    Direct Mining of Rules from Data with Missing Values

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    Pages 265-276

    Cluster Identification Using Maximum Configuration Entropy

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    Pages 277-303

    Mining Small Objects in Large Images Using Neural Networks

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    Pages 305-318

    Improved Knowledge Mining with the Multimethod Approach

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    Pages 319-331

    Posting Act Tagging Using Transformation-Based Learning

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    Pages 333-346

    Identification of Critical Values in Latent Semantic Indexing

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    Book Chapter

    Pages 347-361

    Reporting Data Mining Results in a Natural Language

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