Data Mining, Rough Sets and Granular Computing
(Sprache: Englisch)
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...
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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.
Klappentext zu „Data Mining, Rough Sets and Granular Computing “
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 beviewed as a branch of granular computing. However, its applications to data mining have predated that of granular computing.
Inhaltsverzeichnis zu „Data Mining, Rough Sets and Granular Computing “
1. Granular Computing - A New Paradigm- Some Reflections on Information Granulation and its Centrality in Granular Computing, Computing with Words, the Computational Theory of Perceptions and Precisiated Natural Language
2. Granular Computing in Data Mining
- Data Mining Using Granular Computing: Fast Algorithms for Finding Association Rules
- Knowledge Discovery with Words Using Cartesian Granule Features: An Analysis for Classification Problems
- Validation of Concept Representation with Rule Induction and Linguistic Variables
- Granular Computing Using Information Tables
- A Query-Driven Interesting Rule Discovery Using Association and Spanning Operations
3. Data Mining
- An Interactive Visualization System for Mining Association Rules
- Algorithms for Mining System Audit Data
- Scoring and Ranking the Data Using Association Rules
- Finding Unexpected Patterns in Data
- Discovery of Approximate Knowledge in Medical Databases Based on Rough Set Model
4. Granular Computing
- Observability and the Case of Probability
- Granulation and Granularity via Conceptual Structures: A Perspective From the Point of View of Fuzzy Concept Lattices
- Granular Computing with Closeness and Negligibility Relations
- Application of Granularity Computing to Confirm Compliance with Non-Proliferation Treaty
- Basic Issues of Computing with Granular Probabilities
- Multi-dimensional Aggregation of Fuzzy Numbers Through the Extension Principle
- On Optimal Fuzzy Information Granulation
- Ordinal Decision Making with a Notion of Acceptable: Denoted Ordinal Scales
- A Framework for Building Intelligent Information-Processing Systems Based on Granular Factor Space
5. Rough Sets and Granular Computing
- GRS: A Generalized Rough Sets Model
- Structure of Upper and Lower Approximation Spaces of Infinite Sets
- Indexed Rough Approximations, A Polymodal System, and Generalized Possibility Measures
- Granularity,
... mehr
Multi-valued Logic, Bayes' Theorem and Rough Sets
- The Generic Rough Set Inductive Logic Programming (gRS-ILP) Model
- Possibilistic Data Analysis and Its Similarity to Rough Sets
- The Generic Rough Set Inductive Logic Programming (gRS-ILP) Model
- Possibilistic Data Analysis and Its Similarity to Rough Sets
... weniger
Bibliographische Angaben
- 2002, 2002, 537 Seiten, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Herausgegeben:Lin, Tsau Young; Yao, Yiyu Y.; Zadeh, Lotfi A.
- Herausgegeben: Tsau Young Lin, Lotfi A. Zadeh, Yiyu Y. Yao
- Verlag: Physica-Verlag
- ISBN-10: 379081461X
- ISBN-13: 9783790814613
- Erscheinungsdatum: 10.04.2002
Sprache:
Englisch
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