Fuzzy Fisher Criterion Based Semi-Fuzzy Clustering Algorithm
基于模糊Fisher准则的半模糊聚类算法

The robust Fuzzy Fisher Criterion based Semi-Fuzzy Clustering Algorithm (FFC-SFCA) for linearly separable data is presented in this paper. FFC-SFCA incorporates Fisher discrimination method with fuzzy theory using fuzzy scatter matrix. By iteratively optimizing the fuzzy Fisher criterion function, the final clustering results are obtained. FFC-SFCA exhibits its robustness and capability to obtain well separable clustering results. In addition, optimal discriminant vector and threshold of classifier can also be figured out. The experimental results for artificial and real datasets demonstrate its validity and distinctive superiority over the two conventional clustering algorithms.