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Data Mining

Theory, Methodology, Techniques, and Applications

  • Book
  • © 2006

Overview

Editors:
  1. Graham J. Williams

    1. The Australian Taxation Office,  

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    You can also search for this editor in PubMed   Google Scholar

  2. Simeon J. Simoff

    1. School of Computing and Mathematics, University of Western Sydney, Sydney, Australia

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3755)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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Table of contents (25 chapters)

  1. Front Matter

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  2. Part 1: State-of-the-Art in Research

    1. Methodological Advances

      1. Generality Is Predictive of Prediction Accuracy
        • Geoffrey I. Webb, Damien Brain
        Pages 1-13
      2. Visualisation and Exploration of Scientific Data Using Graphs
        • Ben Raymond, Lee Belbin
        Pages 14-27
      3. A Case-Based Data Mining Platform
        • Xingwen Wang, Joshua Zhexue Huang
        Pages 28-38
      4. Consolidated Trees: An Analysis of Structural Convergence
        • Jesús M. Pérez, Javier Muguerza, Olatz Arbelaitz, Ibai Gurrutxaga, José I. Martín
        Pages 39-52
      5. K Nearest Neighbor Edition to Guide Classification Tree Learning: Motivation and Experimental Results
        • J. M. Martínez-Otzeta, B. Sierra, E. Lazkano, A. Astigarraga
        Pages 53-63
      6. Efficiently Identifying Exploratory Rules’ Significance
        • Shiying Huang, Geoffrey I. Webb
        Pages 64-77
      7. Mining Value-Based Item Packages – An Integer Programming Approach
        • N. R. Achuthan, Raj P. Gopalan, Amit Rudra
        Pages 78-89
      8. Decision Theoretic Fusion Framework for Actionability Using Data Mining on an Embedded System
        • Heungkyu Lee, Sunmee Kang, Hanseok Ko
        Pages 90-104
      9. Use of Data Mining in System Development Life Cycle
        • Richi Nayak, Tian Qiu
        Pages 105-117
      10. Mining MOUCLAS Patterns and Jumping MOUCLAS Patterns to Construct Classifiers
        • Yalei Hao, Gerald Quirchmayr, Markus Stumptner
        Pages 118-129

    2. Data Linkage

      1. A Probabilistic Geocoding System Utilising a Parcel Based Address File
        • Peter Christen, Alan Willmore, Tim Churches
        Pages 130-145
      2. Decision Models for Record Linkage
        • Lifang Gu, Rohan Baxter
        Pages 146-160

    3. Text Mining

      1. Intelligent Document Filter for the Internet
        • Deepani B. Guruge, Russel J. Stonier
        Pages 161-175
      2. Informing the Curious Negotiator: Automatic News Extraction from the Internet
        • Debbie Zhang, Simeon J. Simoff
        Pages 176-191
      3. Text Mining for Insurance Claim Cost Prediction
        • Inna Kolyshkina, Marcel van Rooyen
        Pages 192-202

    4. Temporal and Sequence Mining

      1. An Application of Time-Changing Feature Selection
        • Yihao Zhang, Mehmet A. Orgun, Weiqiang Lin, Warwick Graco
        Pages 203-217
      2. A Data Mining Approach to Analyze the Effect of Cognitive Style and Subjective Emotion on the Accuracy of Time-Series Forecasting
        • Hung Kook Park, Byoungho Song, Hyeon-Joong Yoo, Dae Woong Rhee, Kang Ryoung Park, Juno Chang
        Pages 218-228
      3. A Multi-level Framework for the Analysis of Sequential Data
        • Carl H. Mooney, Denise de Vries, John F. Roddick
        Pages 229-243

  3. Part 2: State-of-the-Art in Applications

    1. Health

      1. Hierarchical Hidden Markov Models: An Application to Health Insurance Data
        • Ah Chung Tsoi, Shu Zhang, Markus Hagenbuchner
        Pages 244-259
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Keywords

Editors and Affiliations

  • The Australian Taxation Office,  

    Graham J. Williams

  • School of Computing and Mathematics, University of Western Sydney, Sydney, Australia

    Simeon J. Simoff

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