Data Mining: Foundations and Practice

ISBN: 978-3-540-78487-6 (Print) 978-3-540-78488-3 (Online)

Table of contents (32 chapters)

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  1. Front Matter

    Pages I-XV

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    Book Chapter

    Pages 1-30

    Compact Representations of Sequential Classification Rules

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    Book Chapter

    Pages 31-48

    An Algorithm for Mining Weighted Dense Maximal 1-Complete Regions

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    Pages 49-60

    Mining Linguistic Trends from Time Series

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    Pages 61-77

    Latent Semantic Space for Web Clustering

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    Pages 79-108

    A Logical Framework for Template Creation and Information Extraction

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    Pages 109-123

    A Bipolar Interpretation of Fuzzy Decision Trees

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    Pages 125-137

    A Probability Theory Perspective on the Zadeh Fuzzy System

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    Book Chapter

    Pages 139-152

    Three Approaches to Missing Attribute Values: A Rough Set Perspective

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    Book Chapter

    Pages 153-164

    MLEM2 Rule Induction Algorithms: With and Without Merging Intervals

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    Book Chapter

    Pages 165-178

    Towards a Methodology for Data Mining Project Development: The Importance of Abstraction

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    Book Chapter

    Pages 179-196

    Fining Active Membership Functions in Fuzzy Data Mining

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    Book Chapter

    Pages 197-211

    A Compressed Vertical Binary Algorithm for Mining Frequent Patterns

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    Pages 213-229

    Naïve Rules Do Not Consider Underlying Causality

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    Pages 231-249

    Inexact Multiple-Grained Causal Complexes

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    Book Chapter

    Pages 251-275

    Does Relevance Matter to Data Mining Research?

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    Book Chapter

    Pages 277-288

    E-Action Rules

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    Pages 289-298

    Mining E-Action Rules, System DEAR

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    Book Chapter

    Pages 299-313

    Definability of Association Rules and Tables of Critical Frequencies

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    Book Chapter

    Pages 315-337

    Classes of Association Rules: An Overview

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    Book Chapter

    Pages 339-352

    Knowledge Extraction from Microarray Datasets Using Combined Multiple Models to Predict Leukemia Types

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