NDLI: A fast approach to building rough data model through G-K fuzzy clustering

Content Provider	IEEE Xplore Digital Library
Author	Jin-Jie Huang Shi-Yong Li Xiao-Jun Ban
Copyright Year	2003
Description	Author affiliation: Dept. of Control Sci. & Eng., Harbin Inst. of Technol., China (Jin-Jie Huang; Shi-Yong Li; Xiao-Jun Ban)
Abstract	A new method to fast build the rough data model (RDM) by means of fuzzy clustering is proposed. The scheme is contrived by Gustafson-Kessel (GK) algorithm, which is of many good properties and is demonstrated in the data-mining context. In this paper, first we investigate how to integrate the RDM's classification quality performance index into the GK clustering algorithm in the product space of input and output variables. Then we suggest the way to convert the fuzzy cluster models to rough data models. Hence, we work out an efficient algorithm that can obtain RDMs by just iteratively computing two necessary condition equations, which can minimize the objective function, and turn the multi-dimensional search process of Kowalczyk's method to one dimensional search strategy (in terms of the number of clusters). This technique reduces the searching time greatly. Moreover, by introducing the concept of the fuzzy degree of fulfillment (DoF) to a cluster rule, our approach seems to be much more flexible and more powerful ability in handling data contaminated by noise, with better generalization ability compared with the traditional rough set theory and the Kowalczyk's method. Finally, two examples illustrate the effectiveness of our approach.
Sponsorship	IEEE Systems Man and Cybernetics Tech. Committee on Cybernetics, Hebei Univ. Northwestern Polytechnical Univ
Starting Page	1559
Ending Page	1564
File Size	447951
Page Count	6
File Format	PDF
ISBN	0780381319
DOI	10.1109/ICMLC.2003.1259743
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-11-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data models Clustering algorithms Pollution measurement Fuzzy control Iterative algorithms Set theory Machine learning Body sensor networks Data engineering Performance analysis
Content Type	Text
Resource Type	Article

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