| 联合模糊c-均值聚类模型 |
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| 作者姓名： | 武小红 周建江 |
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| 作者单位： | [1]南京航空航天大学信息科学与技术学院,中国南京210016 [2]江苏大学电气信息工程学院,中国镇江212013 |
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| 摘 要： | 提出一种新的结合了模糊c-均值聚类（FCM）算法和可能性c-均值聚类（PCM）算法优点的联合模糊c-均值聚类（AFCM）算法。它克服了PCM对初始值敏感、易产生一致性聚类的缺点，是PCM的扩展算法。试验表明：AFCM能同时产生隶属度和典型值，从而更好地处理噪声，避免了一致性聚类，同时提高了聚类准确性。
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| 关 键 词： | 模糊c-均值聚类 可能性c-均值聚类 联合模糊c-均值聚类 |
| 收稿时间： | 2005/10/11 0:00:00 |
| 修稿时间： | 2006/2/18 0:00:00 |
ALLIED FUZZY c-MEANS CLUSTERING MODEL |
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| Authors: | Wu Xiaohong Zhou Jianjiang |
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| Institution: | 1. College of Information Science and Tcchnology, NUAA, 29 Yudao Street, Nanjing, 210016, P. R. China; 2. College of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China |
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| Abstract: | A novel model of fuzzy clustering, i.e. an allied fuzzy c-means (AFCM) model is proposed based on the combination of advantages of fuzzy c-means (FCM) and possibilistic c-means (PCM) clustering. PCM is sensitive to initializations and often generates coincident clusters. AFCM overcomes this shortcoming and it is an extension of PCM. Membership and typicality values can be simultaneously produced in AFCM. Experimental results show that noise data can be well processed, coincident clusters are avoided and clustering accuracy is better. |
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| Keywords: | fuzzy c-means clustering possibilistic c-means clustering allied fuzzy c-means clustering |
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