Ebook: Soft Computing for Knowledge Discovery and Data Mining

Data mining is the science and technology of exploring large and complex bodies of data in order to discover useful and insightful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. Soft Computing for Knowledge Discovery and Data Mining introduces theoretical approaches and practical computing methods extending the envelope of problems that data mining can solve efficiently. From the editors of the leading Data Mining and Knowledge Discovery Handbook, 2005, this volume, by highly regarded authors, includes selected contributors of the Handbook.

The first three parts of this book are devoted to the principal constituents of soft computing: neural networks, evolutionary algorithms and fuzzy logic. The last part compiles the recent advances in soft computing for data mining, such as swarm intelligence, diffusion process and agent technology.

This book was written to provide investigators in the fields of information systems, engineering, computer science, operations research, bio-informatics, statistics and management with a profound source for the role of soft computing in data mining. Not only does this book feature illustrations of various applications including marketing, manufacturing, medical, and others, but it also includes various real-world case studies with detailed results.

Soft Computing for Knowledge Discovery and Data Mining is designed for theoreticians, researchers and advanced practitioners in industry. Practitioners may be particularly interested in the description of real world data mining projects performed with soft computing. This book is also suitable as a textbook or reference for advanced-level students in mathematical quantitative methods in the above fields.

About the editors: Oded Maimon is Full Professor at the Department of Industrial Engineering, Tel-Aviv University, Israel. Lior Rokach is Assistant Professor at the Department of Information System Engineering, Ben-Gurion University of the Negev, Israel. Maimon and Rokach are recognized international experts in data mining and business intelligence, and serve in leading positions in this field. They have written numerous scientific articles and are the editors of the complete Data Mining and Knowledge Discovery Handbook (2005). They have jointly authored two of the best detailed books in the field of data mining: Decomposition Methodology for Knowledge Discovery and Data Mining (2005), and Data Mining with Decision Trees (2007).

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Data mining is the science and technology of exploring large and complex bodies of data in order to discover useful and insightful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. Soft Computing for Knowledge Discovery and Data Mining introduces theoretical approaches and practical computing methods extending the envelope of problems that data mining can solve efficiently. From the editors of the leading Data Mining and Knowledge Discovery Handbook, 2005, this volume, by highly regarded authors, includes selected contributors of the Handbook.

The first three parts of this book are devoted to the principal constituents of soft computing: neural networks, evolutionary algorithms and fuzzy logic.  The last part compiles the recent advances in soft computing for data mining, such as swarm intelligence, diffusion process and agent technology.

This book was written to provide investigators in the fields of information systems, engineering, computer science, operations research, bio-informatics, statistics and management with a profound source for the role of soft computing in data mining. Not only does this book feature illustrations of various applications including marketing, manufacturing, medical, and others, but it also includes various real-world case studies with detailed results.

Soft Computing for Knowledge Discovery and Data Mining is designed for theoreticians, researchers and advanced practitioners in industry.  Practitioners may be particularly interested in the description of real world data mining projects performed with soft computing. This book is also suitable as a textbook or reference for advanced-level students in mathematical quantitative methods in the above fields.

About the editors:  Oded Maimon is Full Professor at the Department of Industrial Engineering, Tel-Aviv University, Israel. Lior Rokach is Assistant Professor at the Department of Information System Engineering, Ben-Gurion University of the Negev, Israel. Maimon and Rokach are recognized international experts in data mining and business intelligence, and serve in leading positions in this field. They have written numerous scientific articles and are the editors of the complete Data Mining and Knowledge Discovery Handbook (2005).  They have jointly authored two of the best detailed books in the field of data mining: Decomposition Methodology for Knowledge Discovery and Data Mining (2005), and Data Mining with Decision Trees (2007).

 

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Data mining is the science and technology of exploring large and complex bodies of data in order to discover useful and insightful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. Soft Computing for Knowledge Discovery and Data Mining introduces theoretical approaches and practical computing methods extending the envelope of problems that data mining can solve efficiently. From the editors of the leading Data Mining and Knowledge Discovery Handbook, 2005, this volume, by highly regarded authors, includes selected contributors of the Handbook.

The first three parts of this book are devoted to the principal constituents of soft computing: neural networks, evolutionary algorithms and fuzzy logic.  The last part compiles the recent advances in soft computing for data mining, such as swarm intelligence, diffusion process and agent technology.

This book was written to provide investigators in the fields of information systems, engineering, computer science, operations research, bio-informatics, statistics and management with a profound source for the role of soft computing in data mining. Not only does this book feature illustrations of various applications including marketing, manufacturing, medical, and others, but it also includes various real-world case studies with detailed results.

Soft Computing for Knowledge Discovery and Data Mining is designed for theoreticians, researchers and advanced practitioners in industry.  Practitioners may be particularly interested in the description of real world data mining projects performed with soft computing. This book is also suitable as a textbook or reference for advanced-level students in mathematical quantitative methods in the above fields.

About the editors:  Oded Maimon is Full Professor at the Department of Industrial Engineering, Tel-Aviv University, Israel. Lior Rokach is Assistant Professor at the Department of Information System Engineering, Ben-Gurion University of the Negev, Israel. Maimon and Rokach are recognized international experts in data mining and business intelligence, and serve in leading positions in this field. They have written numerous scientific articles and are the editors of the complete Data Mining and Knowledge Discovery Handbook (2005).  They have jointly authored two of the best detailed books in the field of data mining: Decomposition Methodology for Knowledge Discovery and Data Mining (2005), and Data Mining with Decision Trees (2007).

 

Content:
Front Matter....Pages I-XIII
Introduction to Soft Computing for Knowledge Discovery and Data Mining....Pages 1-13
Neural Networks For Data Mining....Pages 17-44
Improved SOM Labeling Methodology for Data Mining Applications....Pages 45-75
A Review of evolutionary Algorithms for Data Mining....Pages 79-111
Genetic Clustering for Data Mining....Pages 113-132
Discovering New Rule Induction Algorithms with Grammar-based Genetic Programming....Pages 133-152
evolutionary Design of Code-matrices for Multiclass Problems....Pages 153-184
The Role of Fuzzy Sets in Data Mining....Pages 187-203
Support Vector Machines and Fuzzy Systems....Pages 205-223
KDD in Marketing with Genetic Fuzzy Systems....Pages 225-239
Knowledge Discovery in a Framework for Modelling with Words....Pages 241-276
Swarm Intelligence Algorithms for Data Clustering....Pages 279-313
A Diffusion Framework for Dimensionality Reduction....Pages 315-325
Data Mining and Agent Technology: a fruitful symbiosis....Pages 327-362
Approximate Frequent Itemset Mining In the Presence of Random Noise....Pages 363-389
The Impact of Overfitting and Overgeneralization on the Classification Accuracy in Data Mining....Pages 391-431
Back Matter....Pages 433-433

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Data mining is the science and technology of exploring large and complex bodies of data in order to discover useful and insightful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. Soft Computing for Knowledge Discovery and Data Mining introduces theoretical approaches and practical computing methods extending the envelope of problems that data mining can solve efficiently. From the editors of the leading Data Mining and Knowledge Discovery Handbook, 2005, this volume, by highly regarded authors, includes selected contributors of the Handbook.

The first three parts of this book are devoted to the principal constituents of soft computing: neural networks, evolutionary algorithms and fuzzy logic.  The last part compiles the recent advances in soft computing for data mining, such as swarm intelligence, diffusion process and agent technology.

This book was written to provide investigators in the fields of information systems, engineering, computer science, operations research, bio-informatics, statistics and management with a profound source for the role of soft computing in data mining. Not only does this book feature illustrations of various applications including marketing, manufacturing, medical, and others, but it also includes various real-world case studies with detailed results.

Soft Computing for Knowledge Discovery and Data Mining is designed for theoreticians, researchers and advanced practitioners in industry.  Practitioners may be particularly interested in the description of real world data mining projects performed with soft computing. This book is also suitable as a textbook or reference for advanced-level students in mathematical quantitative methods in the above fields.

About the editors:  Oded Maimon is Full Professor at the Department of Industrial Engineering, Tel-Aviv University, Israel. Lior Rokach is Assistant Professor at the Department of Information System Engineering, Ben-Gurion University of the Negev, Israel. Maimon and Rokach are recognized international experts in data mining and business intelligence, and serve in leading positions in this field. They have written numerous scientific articles and are the editors of the complete Data Mining and Knowledge Discovery Handbook (2005).  They have jointly authored two of the best detailed books in the field of data mining: Decomposition Methodology for Knowledge Discovery and Data Mining (2005), and Data Mining with Decision Trees (2007).

 

Content:
Front Matter....Pages I-XIII
Introduction to Soft Computing for Knowledge Discovery and Data Mining....Pages 1-13
Neural Networks For Data Mining....Pages 17-44
Improved SOM Labeling Methodology for Data Mining Applications....Pages 45-75
A Review of evolutionary Algorithms for Data Mining....Pages 79-111
Genetic Clustering for Data Mining....Pages 113-132
Discovering New Rule Induction Algorithms with Grammar-based Genetic Programming....Pages 133-152
evolutionary Design of Code-matrices for Multiclass Problems....Pages 153-184
The Role of Fuzzy Sets in Data Mining....Pages 187-203
Support Vector Machines and Fuzzy Systems....Pages 205-223
KDD in Marketing with Genetic Fuzzy Systems....Pages 225-239
Knowledge Discovery in a Framework for Modelling with Words....Pages 241-276
Swarm Intelligence Algorithms for Data Clustering....Pages 279-313
A Diffusion Framework for Dimensionality Reduction....Pages 315-325
Data Mining and Agent Technology: a fruitful symbiosis....Pages 327-362
Approximate Frequent Itemset Mining In the Presence of Random Noise....Pages 363-389
The Impact of Overfitting and Overgeneralization on the Classification Accuracy in Data Mining....Pages 391-431
Back Matter....Pages 433-433
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