中文摘要：

文中提出了一种基于单簇可能性C-均值聚类（Possibilistic C—Means，PCM）的数据描述方法并用于单分类．训练时，其首先进行PIM（PCM，C值取1）聚类，得到所有训练样本对目标类的隶属度；然后设置隶属度阈值，形成相应的数据描述进行单分类．分类时，计算新样本对目标类的隶属度，若其隶属度小于该阈值则判为异常，否则为正常．该方法和当前流行的支持向量域数据描述方法以及Parzen方法窗具有类似的参数配置和相当的分类性能，由此提供了另一种单分类学习算法．值得指出的是，尽管是PCM的一个特例，但PIM拥有PCM一般不具备的全局最优特性，而该特性对解决实际问题十分重要．

英文摘要：

In this paper, a one-cluster clustering based data description method （OCCDD） is proposed for one-class classification. It operates as follows： when training, one-cluster Possibilistic C-Means （P1M） algorithm is firstly performed on the training target samples, then the memberships to the target class of all samples are obtained, a threshold of memberships is set to form the data description. When testing, the memberships of the samples for testing are computed, the samples with less membership than the threshold are thought as the outliers, otherwise as the target objects. The proposed method has the same parameter configuration as the prevalent methods. Support Vector Data Description （SVDD） and Parzen-window method, and leads to an alternative one-class classifier. It is worthy to point out that： although as a special example of traditional PCM algorithm, P1M can obtain a globally optimal solution while traditional PCM generally could not. Moreover, the globally optimal property is of great importance for the practical implementation.