Preface | p. V |
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 | p. 3 |
Granular Computing in Data Mining | |
Data Mining Using Granular Computing: Fast Algorithms for Finding Association Rules | p. 23 |
Knowledge Discovery with Words Using Cartesian Granule Features: An Analysis for Classification Problems | p. 46 |
Validation of Concept Representation with Rule Induction and Linguistic Variables | p. 91 |
Granular Computing Using Information Tables | p. 102 |
A Query-Driven Interesting Rule Discovery Using Association and Spanning Operations | p. 125 |
Data Mining | |
An Interactive Visualization System for Mining Association Rules | p. 145 |
Algorithms for Mining System Audit Data | p. 166 |
Scoring and Ranking the Data Using Association Rules | p. 190 |
Finding Unexpected Patterns in Data | p. 216 |
Discovery of Approximate Knowledge in Medical Databases Based on Rough Set Model | p. 232 |
Granular Computing | |
Observability and the Case of Probability | p. 249 |
Granulation and Granularity via Conceptual Structures: A Perspective From the Point of View of Fuzzy Concept Lattices | p. 265 |
Granular Computing with Closeness and Negligibility Relations | p. 290 |
Application of Granularity Computing to Confirm Compliance with Non-Proliferation Treaty | p. 308 |
Basic Issues of Computing with Granular Probabilities | p. 339 |
Multi-dimensional Aggregation of Fuzzy Numbers Through the Extension Principle | p. 350 |
On Optimal Fuzzy Information Granulation | p. 364 |
Ordinal Decision Making with a Notion of Acceptable: Denoted Ordinal Scales | p. 398 |
A Framework for Building Intelligent Information-Processing Systems Based on Granular Factor Space | p. 414 |
Rough Sets and Granular Computing | |
GRS: A Generalized Rough Sets Model | p. 447 |
Structure of Upper and Lower Approximation Spaces of Infinite Sets | p. 461 |
Indexed Rough Approximations, A Polymodal System, and Generalized Possibility Measures | p. 474 |
Granularity, Multi-valued Logic, Bayes' Theorem and Rough Sets | p. 487 |
The Generic Rough Set Inductive Logic Programming (gRS-ILP) Model | p. 499 |
Possibilistic Data Analysis and Its Similarity to Rough Sets | p. 518 |
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