Ebook: Foundations of Computational, IntelligenceVolume 6: Data Mining

Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, business, health care, banking, retail, and many others. Advanced representation schemes and computational intelligence techniques such as rough sets, neural networks; decision trees; fuzzy logic; evolutionary algorithms; artificial immune systems; swarm intelligence; reinforcement learning, association rule mining, Web intelligence paradigms etc. have proved valuable when they are applied to Data Mining problems. Computational tools or solutions based on intelligent systems are being used with great success in Data Mining applications. It is also observed that strong scientific advances have been made when issues from different research areas are integrated.

This Volume comprises of 15 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Intelligence techniques for Data Mining.

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Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, business, health care, banking, retail, and many others. Advanced representation schemes and computational intelligence techniques such as rough sets, neural networks; decision trees; fuzzy logic; evolutionary algorithms; artificial immune systems; swarm intelligence; reinforcement learning, association rule mining, Web intelligence paradigms etc. have proved valuable when they are applied to Data Mining problems. Computational tools or solutions based on intelligent systems are being used with great success in Data Mining applications. It is also observed that strong scientific advances have been made when issues from different research areas are integrated.

This Volume comprises of 15 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Intelligence techniques for Data Mining.

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Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, business, health care, banking, retail, and many others. Advanced representation schemes and computational intelligence techniques such as rough sets, neural networks; decision trees; fuzzy logic; evolutionary algorithms; artificial immune systems; swarm intelligence; reinforcement learning, association rule mining, Web intelligence paradigms etc. have proved valuable when they are applied to Data Mining problems. Computational tools or solutions based on intelligent systems are being used with great success in Data Mining applications. It is also observed that strong scientific advances have been made when issues from different research areas are integrated.

This Volume comprises of 15 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Intelligence techniques for Data Mining.

Content:
Front Matter....Pages -
Front Matter....Pages 1-1
Mining and Analysis of Clickstream Patterns....Pages 3-27
An Overview on Mining Data Streams....Pages 29-45
Data Stream Mining Using Granularity-Based Approach....Pages 47-66
Time Granularity in Temporal Data Mining....Pages 67-96
Mining User Preference Model from Utterances....Pages 97-123
Front Matter....Pages 125-125
Text Summarization: An Old Challenge and New Approaches....Pages 127-149
From Faceted Classification to Knowledge Discovery of Semi-structured Text Records....Pages 151-169
Multi-value Association Patterns and Data Mining....Pages 171-191
Clustering Time Series Data: An Evolutionary Approach....Pages 193-207
Support Vector Clustering: From Local Constraint to Global Stability....Pages 209-227
New Algorithms for Generation Decision Trees—Ant-Miner and Its Modifications....Pages 229-262
Front Matter....Pages 263-263
Automated Incremental Building of Weighted Semantic Web Repository....Pages 265-296
A Data Mining Approach for Adaptive Path Planning on Large Road Networks....Pages 297-320
Linear Models for Visual Data Mining in Medical Images....Pages 321-344
A Framework for Composing Knowledge Discovery Workflows in Grids....Pages 345-369
Distributed Data Clustering: A Comparative Analysis....Pages 371-397
Back Matter....Pages -

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Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, business, health care, banking, retail, and many others. Advanced representation schemes and computational intelligence techniques such as rough sets, neural networks; decision trees; fuzzy logic; evolutionary algorithms; artificial immune systems; swarm intelligence; reinforcement learning, association rule mining, Web intelligence paradigms etc. have proved valuable when they are applied to Data Mining problems. Computational tools or solutions based on intelligent systems are being used with great success in Data Mining applications. It is also observed that strong scientific advances have been made when issues from different research areas are integrated.

This Volume comprises of 15 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Intelligence techniques for Data Mining.

Content:
Front Matter....Pages -
Front Matter....Pages 1-1
Mining and Analysis of Clickstream Patterns....Pages 3-27
An Overview on Mining Data Streams....Pages 29-45
Data Stream Mining Using Granularity-Based Approach....Pages 47-66
Time Granularity in Temporal Data Mining....Pages 67-96
Mining User Preference Model from Utterances....Pages 97-123
Front Matter....Pages 125-125
Text Summarization: An Old Challenge and New Approaches....Pages 127-149
From Faceted Classification to Knowledge Discovery of Semi-structured Text Records....Pages 151-169
Multi-value Association Patterns and Data Mining....Pages 171-191
Clustering Time Series Data: An Evolutionary Approach....Pages 193-207
Support Vector Clustering: From Local Constraint to Global Stability....Pages 209-227
New Algorithms for Generation Decision Trees—Ant-Miner and Its Modifications....Pages 229-262
Front Matter....Pages 263-263
Automated Incremental Building of Weighted Semantic Web Repository....Pages 265-296
A Data Mining Approach for Adaptive Path Planning on Large Road Networks....Pages 297-320
Linear Models for Visual Data Mining in Medical Images....Pages 321-344
A Framework for Composing Knowledge Discovery Workflows in Grids....Pages 345-369
Distributed Data Clustering: A Comparative Analysis....Pages 371-397
Back Matter....Pages -
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