Comparison of different strategies of utilizing fuzzy clustering in structure identification

Kılıç, Kemal and Uncu, Özge and Türkşen, I. Burhan (2007) Comparison of different strategies of utilizing fuzzy clustering in structure identification. Information Sciences, 177 (23). pp. 5153-5162. ISSN 0020-0255

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Fuzzy systems approximate highly nonlinear systems by means of fuzzy "if-then" rules. In the literature, various algorithms are proposed for mining. These algorithms commonly utilize fuzzy clustering in structure identification. Basically, there are three different approaches in which one can utilize fuzzy clustering; the �first one is based on input space clustering, the second one considers clustering realized in the output space, while the third one is concerned with clustering realized in the combined input-output space. In this study, we analyze these three approaches. We discuss each of the algorithms in great detail and o¤er a thorough comparative analysis. Finally, we compare the performances of these algorithms in a medical diagnosis classi�cation problem, namely Aachen Aphasia Test. The experiment and the results provide a valuable insight about the merits and the shortcomings of these three clustering approaches.
Item Type: Article
Uncontrolled Keywords: fuzzy system modelling; medicine; knowledge acquisition; data mining; structure identification
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Kemal Kılıç
Date Deposited: 16 Nov 2007 12:18
Last Modified: 26 Apr 2022 08:18

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