Data Mining, Rough Sets and Granular Computing

✍ Scribed by Lotfi A. Zadeh (auth.), Professor Tsau Young Lin, Professor Yiyu Y. Yao, Professor Lotfi A. Zadeh (eds.)

• Publisher: Physica-Verlag Heidelberg
• Year: 2002
• Tongue: English
• Leaves: 538
• Series: Studies in Fuzziness and Soft Computing 95
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages I-IX
Front Matter....Pages 1-1
Some Reflections on Information Granulation and its Centrality in Granular Computing, Computing with Words, the Computational Theory of Perceptions and Precisiated Natural Language....Pages 3-20
Front Matter....Pages 21-21
Data Mining Using Granular Computing: Fast Algorithms for Finding Association Rules....Pages 23-45
Knowledge Discovery with Words Using Cartesian Granule Features: An Analysis for Classification Problems....Pages 46-90
Validation of Concept Representation with Rule Induction and Linguistic Variables....Pages 91-101
Granular Computing Using Information Tables....Pages 102-124
A Query-Driven Interesting Rule Discovery Using Associations and Spanning Operations....Pages 125-141
Front Matter....Pages 143-143
An Interactive Visualization System for Mining Association Rules....Pages 145-165
Algorithms for Mining System Audit Data....Pages 166-189
Scoring and Ranking the Data Using Association Rules....Pages 190-215
Finding Unexpected Patterns in Data....Pages 216-231
Discovery of Approximate Knowledge in Medical Databases Based on Rough Set Model....Pages 232-246
Front Matter....Pages 247-247
Observability and the Case of Probability....Pages 249-264
Granulation and Granularity via Conceptual Structures: A Perspective From the Point of View of Fuzzy Concept Lattices....Pages 265-289
Granular Computing with Closeness and Negligibility Relations....Pages 290-307
Application of Granularity Computing to Confirm Compliance with Non-Proliferation Treaty....Pages 308-338
Basic Issues of Computing with Granular Probabilities....Pages 339-349
Multi-dimensional Aggregation of Fuzzy Numbers Through the Extension Principle....Pages 350-363
On Optimal Fuzzy Information Granulation....Pages 364-397
Ordinal Decision Making with a Notion of Acceptable: Denoted Ordinal Scales....Pages 398-413
A Framework for Building Intelligent Information-Processing Systems Based on Granular Factors Space....Pages 414-444
Front Matter....Pages 445-445
GRS: A Generalized Rough Sets Model....Pages 447-460
Structure of Upper and Lower Approximation Spaces of Infinite Sets....Pages 461-473
Indexed Rough Approximations, A Polymodal System, and Generalized Possibility Measures....Pages 474-486
Granularity, Multi-valued Logic, Bayes’ Theorem and Rough Sets....Pages 487-498
The Generic Rough Set Inductive Logic Programming (gRS—ILP) Model....Pages 499-517
Possibilistic Data Analysis and Its Similarity to Rough Sets....Pages 518-536
Back Matter....Pages 536-536

✦ Subjects

Artificial Intelligence (incl. Robotics); Database Management