Data Mining, Rough Sets and Granular Computing

by Tsau Young Lin (Editor)

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 Fast Algorithms for Finding Association Rules.- Knowledge Discovery with Words Using Cartesian Granule 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 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 Denoted Ordinal Scales.- A Framework for Building Intelligent Information-Processing Systems Based on Granular Factor Space.- 5: Rough Sets and Granular Computing.- 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, 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.

548 pages, Paperback

Published March 12, 2014