KFCSA: A novel clustering algorithm for high-dimension data

Classical fuzzy c-means and its variants cannot get better effect when the characteristic of samples is not obvious, and these algorithms run easily into locally optimal solution. According to the drawbacks, a novel mercer kernel based fuzzy clustering self-adaptive algorithm(KFCSA) is presented. Mercer kernel method is used to map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. A self-adaptive algorithm is proposed to decide the number of clusters, which is not given in advance, and it can be gotten automatically by a validity measure function. In addition, attribute reduction algorithm is used to decrease the numbers of attributes before high dimensional data are clustered. Finally, experiments indicate that KFCSA may get better performance.