Fuzzy K-Prototypes Algorithm for Clustering Mixed Numeric and Categorical Valued Data
An Chen · 2001
The capacity of dealing with mixed numeric and categorical valued data is undoubtedly important for clustering algorithms because there is usually a mixture of numeric and categorical valued attributes in real databases. The use of fuzzy techniques makes clustering algorithms robust against noise and missing values in the databases. In this paper, a fuzzy kprototypes algorithm integrating k means and k modes algorithm is presented and is used to mixed databases. Experiments on several real databases demonstrate that fuzzy algorithm can get better result than the corresponding hard algorithm. Some properties of fuzzy k prototypes algorithm are also discussed.