关键词: 加权指数, FKP算法, 聚类有效性

Abstract: Fuzzy K-Prototypes(FKP) algorithm integrating K-Means and K-Modes algorithm is suited for clustering mixed numeric and categorical valued data. The use of fuzzy techniques makes it robust against noise and missing values in the databases. But, it is an open problem how to select an appropriate weighting exponent α when run FCM(Fuzzy C-Means algorithm) or FKP. Some researchers have suggested that the best choice for α in FCM be probably in the interval \ based on their experimental results. In this paper, the algorithm for searching suitable α in FKP was presented. The experimental results on several real datasets show that the valid clustering can be achieved when α is under 1.5.

Key words: weighting exponent, FKP algorithm, clustering validity