Data Mining, Rough Sets and Granular Computing

1. Front Matter

   Pages I-IX

   PDF

2. Granular Computing — A New Paradigm

   1. Front Matter

      Pages 1-1

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   2. Some Reflections on Information Granulation and its Centrality in Granular Computing, Computing with Words, the Computational Theory of Perceptions and Precisiated Natural Language

      • Lotfi A. Zadeh

      Pages 3-20

3. Granular Computing in Data Mining

   1. Front Matter

      Pages 21-21

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   2. Data Mining Using Granular Computing: Fast Algorithms for Finding Association Rules

      • T. Y. Lin, Eric Louie

      Pages 23-45

   3. Knowledge Discovery with Words Using Cartesian Granule Features: An Analysis for Classification Problems

      • James G. Shanahan

      Pages 46-90

   4. Validation of Concept Representation with Rule Induction and Linguistic Variables

      • Shusaku Tsumoto

      Pages 91-101

   5. Granular Computing Using Information Tables

      • Y. Y. Yao, Ning Zhong

      Pages 102-124

   6. A Query-Driven Interesting Rule Discovery Using Associations and Spanning Operations

      • Jong P. Yoon, Larry Kerschberg

      Pages 125-141

4. Data Mining

   1. Front Matter

      Pages 143-143

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   2. An Interactive Visualization System for Mining Association Rules

      • Jianchao Han, Nick Cercone, Xiaohua Hu

      Pages 145-165

   3. Algorithms for Mining System Audit Data

      • Wenke Lee, Salvatore J. Stolfo, Kui W. Mok

      Pages 166-189

   4. Scoring and Ranking the Data Using Association Rules

      • Bing Liu, Yiming Ma, Ching Kian Wong

      Pages 190-215

   5. Finding Unexpected Patterns in Data

      • Balaji Padmanabhan, Alexander Tuzhilin

      Pages 216-231

   6. Discovery of Approximate Knowledge in Medical Databases Based on Rough Set Model

      • Shusaku Tsumoto

      Pages 232-246

5. Granular Computing

   1. Front Matter

      Pages 247-247

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   2. Observability and the Case of Probability

      • Claudi Alsina, Joan Jacas, Enric Trillas

      Pages 249-264

   3. Granulation and Granularity via Conceptual Structures: A Perspective From the Point of View of Fuzzy Concept Lattices

      • Radim Bělohlávek

      Pages 265-289

   4. Granular Computing with Closeness and Negligibility Relations

      • Didier Dubois, Allel Hadj-Ali, Henri Prade

      Pages 290-307

   5. Application of Granularity Computing to Confirm Compliance with Non-Proliferation Treaty

      • A. Fattah, V. Pouchkarev, A. Belenki, A. Ryjov, L. A. Zadeh

      Pages 308-338

During the past few years, data mining has grown rapidly in visibility and importance within information processing and decision analysis. This is par­ ticularly true in the realm of e-commerce, where data mining is moving from a "nice-to-have" to a "must-have" status. In a different though related context, a new computing methodology called granular computing is emerging as a powerful tool for the conception, analysis and design of information/intelligent systems. In essence, data mining deals with summarization of information which is resident in large data sets, while granular computing plays a key role in the summarization process by draw­ ing together points (objects) which are related through similarity, proximity or functionality. In this perspective, granular computing has a position of centrality in data mining. Another methodology which has high relevance to data mining and plays a central role in this volume is that of rough set theory. Basically, rough set theory may be viewed as a branch of granular computing. However, its applications to data mining have predated that of granular computing.

• Extension
• Scoring
• approximation
• calculus
• classification
• data analysis
• data mining
• fuzzy sets
• knowledge discovery
• logic
• logic programming
• natural language
• perception
• programming
• visualization

• Department of Mathematics and Computer Science, San Jose State University The Metropolitan University of Silicon Valley, San Jose, USA

  Tsau Young Lin

• Department of Computer Science, University of Regina, Regina, Canada

  Yiyu Y. Yao

• Computer Science Division and Electronics Research Laboratory Department of Electrical and Electronics, University of California Berkeley Initiative in Soft Computing (BISC), Berkeley, USA

  Lotfi A. Zadeh

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