Data Mining

Foundations and Practice

By: Tsau Young Lin (Editor), Ying Xie (Editor), Anita Wasilewska (Editor)

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Hardcover | 20 August 2008

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Hardcover
580 Pages

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23.5 x 15.88 x 3.18

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This book contains valuable studies in data mining from both foundational and practical perspectives. The foundational studies of data mining may help to lay a solid foundation for data mining as a scientific discipline, while the practical studies of data mining may lead to new data mining paradigms and algorithms.

The foundational studies contained in this book focus on a broad range of subjects, including conceptual framework of data mining, data preprocessing and data mining as generalization, probability theory perspective on fuzzy systems, rough set methodology on missing values, inexact multiple-grained causal complexes, complexity of the privacy problem, logical framework for template creation and information extraction, classes of association rules, pseudo statistical independence in a contingency table, and role of sample size and determinants in granularity of contingency matrix.

The practical studies contained in this book cover different fields of data mining, including rule mining, classification, clustering, text mining, Web mining, data stream mining, time series analysis, privacy preservation mining, fuzzy data mining, ensemble approaches, and kernel based approaches.

We believe that the works presented in this book will encourage the study of data mining as a scientific field and spark collaboration among researchers and practitioners.

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Compact Representations of Sequential Classification Rules	p. 1
An Algorithm for Mining Weighted Dense Maximal 1-Complete Regions	p. 31
Mining Linguistic Trends from Time Series	p. 49
Latent Semantic Space for Web Clustering	p. 61
A Logical Framework for Template Creation and Information Extraction	p. 79
A Bipolar Interpretation of Fuzzy Decision Trees	p. 109
A Probability Theory Perspective on the Zadeh Fuzzy System	p. 125
Three Approaches to Missing Attribute Values: A Rough Set Perspective	p. 139
MLEM2 Rule Induction Algorithms: With and Without Merging Intervals	p. 153
Towards a Methodology for Data Mining Project Development: The Importance of Abstraction	p. 165
Fining Active Membership Functions in Fuzzy Data Mining	p. 179
A Compressed Vertical Binary Algorithm for Mining Frequent Patterns	p. 197
Naive Rules Do Not Consider Underlying Causality	p. 213
Inexact Multiple-Grained Causal Complexes	p. 231
Does Relevance Matter to Data Mining Research?	p. 251
E-Action Rules	p. 277
Mining E-Action Rules, System DEAR	p. 289
Definability of Association Rules and Tables of Critical Frequencies	p. 299
Classes of Association Rules: An Overview	p. 315
Knowledge Extraction from Microarray Datasets Using Combined Multiple Models to Predict Leukemia Types	p. 339
On the Complexity of the Privacy Problem in Databases	p. 353
Ensembles of Least Squares Classifiers with Randomized Kernels	p. 375
On Pseudo-Statistical Independence in a Contingency Table	p. 387
Role of Sample Size and Determinants in Granularity of Contingency Matrix	p. 405
Generating Concept Hierarchies from User Queries	p. 423
Mining Efficiently Significant Classification Association Rules	p. 443
Data Preprocessing and Data Mining as Generalization	p. 469
Capturing Concepts and Detecting Concept-Drift from Potential Unbounded, Ever-Evolving and High-Dimensional Data Streams	p. 485
A Conceptual Framework of Data Mining	p. 501
How to Prevent Private Data from being Disclosed to a Malicious Attacker	p. 517
Privacy-Preserving Naive Bayesian Classification over Horizontally Partitioned Data	p. 529
Using Association Rules for Classification from Databases Having Class Label Ambiguities: A Belief Theoretic Method	p. 539
Table of Contents provided by Ingram. All Rights Reserved.

Data Mining

Foundations and Practice

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

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