Knowledge extraction and representation using quantum mechanics and intelligent models

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In this paper, we elaborate on the systematic design of approaches that combine quantum clustering with intelligent models for knowledge extraction, learning, and representation. Clustering techniques, which acquire certain characteristics of input data, are efficient methods of extracting knowledge from numerical data sets. They can obtain information in the form of cluster centers or relevant structural parameters. The structure and parameters are easily transformed into the initial knowledge of intelligent models. In particular, quantum clustering does not depend on conventional probability approaches but infers the centers of clusters on the basis of the Schrodinger wave equation from quantum mechanics. When used for knowledge extraction, quantum clustering can determine the cluster centers by searching for minima of the potential functions in quantum mechanics. We apply the characteristics of quantum clustering to well-known intelligent models such as the Takagi-Sugeno-Kang (TSK) fuzzy model, the zero-order fuzzy model, and the radial basis function network (RBFN) to facilitate knowledge representation. To show the usefulness of the proposed approaches in knowledge management (or extraction and representation), we use benchmark data sets and compare our results with those of previous work. (C) 2011 Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2012-02
Language
English
Article Type
Article
Keywords

IDENTIFICATION; DESIGN; SPACE

Citation

EXPERT SYSTEMS WITH APPLICATIONS, v.39, no.3, pp.3572 - 3581

ISSN
0957-4174
DOI
10.1016/j.eswa.2011.09.047
URI
http://hdl.handle.net/10203/100750
Appears in Collection
CS-Journal Papers(저널논문)
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