Foundations of Computational Intelligence

1. Front Matter

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2. Data Click Streams and Temporal Data Mining

   1. Front Matter

      Pages 1-1

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   2. Mining and Analysis of Clickstream Patterns

      • H. Hannah Inbarani, K. Thangavel

      Pages 3-27

   3. An Overview on Mining Data Streams

      • João Gama, Pedro Pereira Rodrigues

      Pages 29-45

   4. Data Stream Mining Using Granularity-Based Approach

      • Mohamed Medhat Gaber

      Pages 47-66

   5. Time Granularity in Temporal Data Mining

      • Paul Cotofrei, Kilian Stoffel

      Pages 67-96

   6. Mining User Preference Model from Utterances

      • Yasufumi Takama, Yuki Muto

      Pages 97-123

3. Text and Rule Mining

   1. Front Matter

      Pages 125-125

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   2. Text Summarization: An Old Challenge and New Approaches

      • Josef Steinberger, Karel Ježek

      Pages 127-149

   3. From Faceted Classification to Knowledge Discovery of Semi-structured Text Records

      • Yee Mey Goh, Matt Giess, Chris McMahon, Ying Liu

      Pages 151-169

   4. Multi-value Association Patterns and Data Mining

      • Thomas W. H. Lui, David K. Y. Chiu

      Pages 171-191

   5. Clustering Time Series Data: An Evolutionary Approach

      • Monica Chiş, Soumya Banerjee, Aboul Ella Hassanien

      Pages 193-207

   6. Support Vector Clustering: From Local Constraint to Global Stability

      • Bahman Yari Saeed Khanloo, Daryanaz Dargahi, Nima Aghaeepour, Ali Masoudi-Nejad

      Pages 209-227

   7. New Algorithms for Generation Decision Trees—Ant-Miner and Its Modifications

      • Urszula Boryczka, Jan Kozak

      Pages 229-262

4. Data Mining Applications

   1. Front Matter

      Pages 263-263

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   2. Automated Incremental Building of Weighted Semantic Web Repository

      • Martin Řimnáč, Roman Špánek

      Pages 265-296

   3. A Data Mining Approach for Adaptive Path Planning on Large Road Networks

      • A. Awasthi, S. S. Chauhan, M. Parent, Y. Lechevallier, J. M. Proth

      Pages 297-320

   4. Linear Models for Visual Data Mining in Medical Images

      • Alexei Manso Corrêa Machado

      Pages 321-344

   5. A Framework for Composing Knowledge Discovery Workflows in Grids

      • Marco Lackovic, Domenico Talia, Paolo Trunfio

      Pages 345-369

   6. Distributed Data Clustering: A Comparative Analysis

      • N. Karthikeyani Visalakshi, K. Thangavel

      Pages 371-397

Foundations of Computational Intelligence Volume 6: Data Mining: Theoretical Foundations and Applications 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; arti- cial immune systems; swarm intelligence; reinforcement learning, association rule mining, Web intelligence paradigms etc. have proved valuable when they are - plied to Data Mining problems. Computational tools or solutions based on intel- gent 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 Int- ligence techniques for Data Mining. The book is divided into 3 parts: Part-I: Data Click Streams and Temporal Data Mining Part-II: Text and Rule Mining Part-III: Applications Part I on Data Click Streams and Temporal Data Mining contains four chapters that describe several approaches in Data Click Streams and Temporal Data Mining.

• algorithm
• algorithms
• bioinformatics
• classification
• computational intelligence
• data mining
• decision tree
• evolution
• intelligence
• knowledge discovery
• machine learning
• model
• neural networks
• semantic web
• statistics

• Machine Intelligence Research Labs, (MIR Labs), Scientific Network for Innovation and Research Excellence, Washington, USA

  Ajith Abraham

• College of Business Administration, Quantitative and Information System Department, Kuwait University, Safat, Kuwait

  Aboul-Ella Hassanien

• Department of Computer Science, University of São Paulo, Sao Carlos, Brazil

  André Ponce Leon F. de Carvalho

• Dept. Computer Science, Technical University Ostrava, Ostrava, Czech Republic

  Václav Snášel

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