期刊
INFORMATION SCIENCES
卷 177, 期 23, 页码 5153-5162出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2007.06.030
关键词
fuzzy system modelling; medicine; knowledge acquisition; data mining; structure identification
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 offer a thorough comparative analysis. Finally, we compare the performances of these algorithms in a medical diagnosis classification 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. (C) 2007 Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据