| Fuzzy Clustering with Novel Separable Criterion |
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| 作者姓名： | 尹中航 唐元钢 孙富春 孙增圻 |
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| 作者单位： | Department of Computer Science and Technology Tsinghua University，Beijing 100084 China，Department of Computer Science and Technology Tsinghua University，Beijing 100084 China，Department of Computer Science and Technology Tsinghua University，Beijing 100084 China，Department of Computer Science and Technology Tsinghua University，Beijing 100084 China |
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| 基金项目： | Supported by the National Excellent Doctoral Dissertation Foundation(No. 200041) and the National Key Basic Research and Development (973) Program of China (No. G2002cb312205) |
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| 摘 要： | Introduction Fuzzy clustering plays an important role in pattern rec ognition, image processing, and data analysis. In fuzzy clustering, every point is assigned a membership to represent the degree of belonging to a certain class The fuzzy c-means (FCM) m…
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| 关 键 词： | 模糊聚类 可分标准 FCM 交互最优化 模式识别 |
| 收稿时间： | 2004-09-15 |
| 修稿时间： | 2004-09-152005-01-18 |
Fuzzy Clustering with Novel Separable Criterion |
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| Zhonghang Yin, í
, Yuangang Tang, &#x;&#x;
, Fuchun Sun, ௌ&#x;,Zengqi Sun, .Fuzzy Clustering with Novel Separable Criterion[J].Tsinghua Science and Technology,2006,11(1):50-53. |
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| Authors: | Zhonghang Yin í
Yuangang Tang &#x;&#x;
Fuchun Sun ௌ&#x; Zengqi Sun |
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| Institution: | Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China |
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| Abstract: | Fuzzy clustering has been used widely in pattern recognition, image processing, and data analysis. An improved fuzzy clustering algorithm was developed based on the conventional fuzzy c-means (FCM) to obtain better quality clustering results. The update equations for the membership and the cluster center are derived from the alternating optimization algorithm. Two fuzzy scattering matrices in the objective function assure the compactness between data points and cluster centers, and also strengthen the separation between cluster centers in terms of a novel separable criterion. The clustering algorithm properties are shown to be an improvement over the FCM method's properties. Numerical simulations show that the clustering algorithm gives more accurate clustering results than the FCM method. |
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| Keywords: | fuzzy c-means (FCM) alternating optimization fuzzy clustering |
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