Abstract: Fuzzy C-Means（FCM） algorithm is sensitive to noise and Possibilistic C-Means（PCM） algorithm is very sensitive to the initialization of cluster center and generates coincident clusters. With the collaborative relations among different feature subsets, the collaborative fuzzy clustering is combined with other clustering algorithms to make its clustering result better than that of the one with the original algorithm. An improved fuzzy clustering algorithm is proposed based on the combination of PCM and collaborative fuzzy clustering. The experimental results on the data sets show the effectiveness of the proposed method.

Key words: Possibilistic C-Means clustering（PCM）, Fuzzy C-Means（FCM）, collaborative fuzzy clustering

摘要： 模糊C-均值聚类（FCM）对噪声数据敏感和可能性C-均值聚类（PCM）对初始中心非常敏感易导致一致性聚类。协同聚类算法利用不同特征子集之间的协同关系并与其他算法相结合，可提高原有的聚类性能。对此，在可能性C-均值聚类算法（PCM）基础上将其与协同聚类算法相结合，提出一种协同的可能性C-均值模糊聚类算法（C-FCM）。该算法在改进的PCM的基础上，提高了对数据集的聚类效果。在对数据集Wine和Iris进行测试的结果表明，该方法优于PCM算法，说明该算法的有效性。

关键词: 可能性C-均值聚类（PCM）, 模糊C均值（FCM）, 协同模糊聚类