Ebook: Data Mining: Foundations and Practice

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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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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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.

Content:
Front Matter....Pages I-XV
Compact Representations of Sequential Classification Rules....Pages 1-30
An Algorithm for Mining Weighted Dense Maximal 1-Complete Regions....Pages 31-48
Mining Linguistic Trends from Time Series....Pages 49-60
Latent Semantic Space for Web Clustering....Pages 61-77
A Logical Framework for Template Creation and Information Extraction....Pages 79-108
A Bipolar Interpretation of Fuzzy Decision Trees....Pages 109-123
A Probability Theory Perspective on the Zadeh Fuzzy System....Pages 125-137
Three Approaches to Missing Attribute Values: A Rough Set Perspective....Pages 139-152
MLEM2 Rule Induction Algorithms: With and Without Merging Intervals....Pages 153-164
Towards a Methodology for Data Mining Project Development: The Importance of Abstraction....Pages 165-178
Fining Active Membership Functions in Fuzzy Data Mining....Pages 179-196
A Compressed Vertical Binary Algorithm for Mining Frequent Patterns....Pages 197-211
Na?ve Rules Do Not Consider Underlying Causality....Pages 213-229
Inexact Multiple-Grained Causal Complexes....Pages 231-249
Does Relevance Matter to Data Mining Research?....Pages 251-275
E-Action Rules....Pages 277-288
Mining E-Action Rules, System DEAR....Pages 289-298
Definability of Association Rules and Tables of Critical Frequencies....Pages 299-313
Classes of Association Rules: An Overview....Pages 315-337
Knowledge Extraction from Microarray Datasets Using Combined Multiple Models to Predict Leukemia Types....Pages 339-352
On the Complexity of the Privacy Problem in Databases....Pages 353-374
Ensembles of Least Squares Classifiers with Randomized Kernels....Pages 375-386
On Pseudo-Statistical Independence in a Contingency Table....Pages 387-403
Role of Sample Size and Determinants in Granularity of Contingency Matrix....Pages 405-421
Generating Concept Hierarchies from User Queries....Pages 423-441
Mining Efficiently Significant Classification Association Rules....Pages 443-467
Data Preprocessing and Data Mining as Generalization....Pages 469-484
Capturing Concepts and Detecting Concept-Drift from Potential Unbounded, Ever-Evolving and High-Dimensional Data Streams....Pages 485-499
A Conceptual Framework of Data Mining....Pages 501-515
How to Prevent Private Data from being Disclosed to a Malicious Attacker....Pages 517-528
Privacy-Preserving Naive Bayesian Classification over Horizontally Partitioned Data....Pages 529-538
Using Association Rules for Classification from Databases Having Class Label Ambiguities: A Belief Theoretic Method....Pages 539-562

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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.

Content:
Front Matter....Pages I-XV
Compact Representations of Sequential Classification Rules....Pages 1-30
An Algorithm for Mining Weighted Dense Maximal 1-Complete Regions....Pages 31-48
Mining Linguistic Trends from Time Series....Pages 49-60
Latent Semantic Space for Web Clustering....Pages 61-77
A Logical Framework for Template Creation and Information Extraction....Pages 79-108
A Bipolar Interpretation of Fuzzy Decision Trees....Pages 109-123
A Probability Theory Perspective on the Zadeh Fuzzy System....Pages 125-137
Three Approaches to Missing Attribute Values: A Rough Set Perspective....Pages 139-152
MLEM2 Rule Induction Algorithms: With and Without Merging Intervals....Pages 153-164
Towards a Methodology for Data Mining Project Development: The Importance of Abstraction....Pages 165-178
Fining Active Membership Functions in Fuzzy Data Mining....Pages 179-196
A Compressed Vertical Binary Algorithm for Mining Frequent Patterns....Pages 197-211
Na?ve Rules Do Not Consider Underlying Causality....Pages 213-229
Inexact Multiple-Grained Causal Complexes....Pages 231-249
Does Relevance Matter to Data Mining Research?....Pages 251-275
E-Action Rules....Pages 277-288
Mining E-Action Rules, System DEAR....Pages 289-298
Definability of Association Rules and Tables of Critical Frequencies....Pages 299-313
Classes of Association Rules: An Overview....Pages 315-337
Knowledge Extraction from Microarray Datasets Using Combined Multiple Models to Predict Leukemia Types....Pages 339-352
On the Complexity of the Privacy Problem in Databases....Pages 353-374
Ensembles of Least Squares Classifiers with Randomized Kernels....Pages 375-386
On Pseudo-Statistical Independence in a Contingency Table....Pages 387-403
Role of Sample Size and Determinants in Granularity of Contingency Matrix....Pages 405-421
Generating Concept Hierarchies from User Queries....Pages 423-441
Mining Efficiently Significant Classification Association Rules....Pages 443-467
Data Preprocessing and Data Mining as Generalization....Pages 469-484
Capturing Concepts and Detecting Concept-Drift from Potential Unbounded, Ever-Evolving and High-Dimensional Data Streams....Pages 485-499
A Conceptual Framework of Data Mining....Pages 501-515
How to Prevent Private Data from being Disclosed to a Malicious Attacker....Pages 517-528
Privacy-Preserving Naive Bayesian Classification over Horizontally Partitioned Data....Pages 529-538
Using Association Rules for Classification from Databases Having Class Label Ambiguities: A Belief Theoretic Method....Pages 539-562
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