This volume is the result of a two-year project aimed at coalescing the concepts and techniques of granular computing on one side, and rough set theory on another. It consists of a collection of up-to-date and authoritative expositions of the basic theories underlying data mining, granular computing and rough set theory, and stresses their wide-ranging applications. A principal aim of the work is to stimulate an exploration of ways in which progress in data mining can be enhanced through integration with granular computing and rough set theory.
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Key Phrases - Statistically Improbable Phrases (SIPs):
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granule feature models, granular probabilities, lower approximation spaces, new audit data, upper approximation space, granule features, frequent ruleitems, discretizing numerical attributes, exponential possibility distributions, granule characterisation, lower possibility distributions, trapezoidal fuzzy sets, evidential logic rule, positive class rule, polymodal system, probability granule, granule fuzzy, rough modus ponens, domain input features, association rule model, soft decision rule, fuzzy information granulation, elementary granules, lift index, crisp granules Key Phrases - Capitalized Phrases (CAPs): (learn more)
International Conference, New York, Information Sciences, Kluwer Academic Publishers, Management of Data, Data Engineering, Member States, Academic Press, Chengzhong Luo, Morgan Kaufmann, Palo Alto, Proof of Proposition, John Wiley, San Jose, University of California, International Workshop, Knowledge-Based Systems, Lecture Notes, Theoretical Aspects of Reasoning, Bulletin of Polish Academy of Science, Column Names, Int'l Conf, New Generation, North Carolina, Peizhuang Wang New!
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Front Cover | Table of Contents | First Pages | Back Cover | Surprise Me!
granule feature models, granular probabilities, lower approximation spaces, new audit data, upper approximation space, granule features, frequent ruleitems, discretizing numerical attributes, exponential possibility distributions, granule characterisation, lower possibility distributions, trapezoidal fuzzy sets, evidential logic rule, positive class rule, polymodal system, probability granule, granule fuzzy, rough modus ponens, domain input features, association rule model, soft decision rule, fuzzy information granulation, elementary granules, lift index, crisp granules Key Phrases - Capitalized Phrases (CAPs): (learn more)
International Conference, New York, Information Sciences, Kluwer Academic Publishers, Management of Data, Data Engineering, Member States, Academic Press, Chengzhong Luo, Morgan Kaufmann, Palo Alto, Proof of Proposition, John Wiley, San Jose, University of California, International Workshop, Knowledge-Based Systems, Lecture Notes, Theoretical Aspects of Reasoning, Bulletin of Polish Academy of Science, Column Names, Int'l Conf, New Generation, North Carolina, Peizhuang Wang New!
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Front Cover | Table of Contents | First Pages | Back Cover | Surprise Me!
Citations (learn more)
This book cites 72
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23
books
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- Rough Sets by Lech Polkowski on 7 pages
- Advances in Knowledge Discovery and Data Mining (American Association for Artificial Intelligence) by Usama M. Fayyad on 6 pages
- Knowledge Discovery in Databases (Aaai Press Copublications) by Gregory Piatetsky-Shapiro on 5 pages
- Data Mining by Jiawei Han on 5 pages
- Soft Computing on 5 pages
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- Intelligent Technologies for Information Analysis by Ning Zhong on page 15, page 130, and page 472
- Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 10th International Conference, RSFDGrC 2005, Regina, Canada, August 31 - September 3, ... / Lecture Notes in Artificial Intelligence) by Dominik Slezak on page 274, and page 717
- Transactions on Rough Sets I (Lecture Notes in Computer Science / Transactions on Rough Sets) on page 253, and page 335
- Clustering and Information Retrieval (Network Theory and Applications) by Weili Wu in Back Matter (1), and Back Matter (2)
- Rough-Neuro-Computing by Sankar Kumar Pal on page 79, and page 516
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