Data Mining, Rough Sets and Granular Computing

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Lin, Tsau Young (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Yao, Yiyu Y. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Zadeh, Lotfi A. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Heidelberg : Physica-Verlag HD : Imprint: Physica, 2002.
• Έκδοση: 1st ed. 2002.
• Σειρά: Studies in Fuzziness and Soft Computing, 95
• Θέματα: Artificial intelligence.; Database management.; Artificial Intelligence.; Database Management.

 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. 

 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, 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.