One-Cluster Clustering Based Data Description
Bin Chen · Chinese Journal of Computers · 2007
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.