Abstract:

Classic fuzzy C-means clustering is a noise-data-sensitive algorithm, which does not take the imbalances among characteristics of samples into consideration and is not suitable for clustering high dimensional data. The possibilistic clustering solves the noisesensitive and consistency of clustering problems but it is under the assumption that each sample has the same contribution to the clustering. Therefore, a samplefeature weighted possibilistic fuzzy kernel clustering algorithm is proposed. The possibilistic clustering is applied to fuzzy clustering in order to improve the antiinterference ability of noise or exceptional points, meanwhile, according to the specific characteristics of different types, the importance of each sample characteristic upon different types is measured dynamically, as well as the importance of each sample upon different cluster, and the optimal nuclear parameters is selected. To map the nonlinearseparable data cluster in the original space to the homogeneous data cluster in the highdimensional space, the kernel functions are modified constantly. The experimental results show that the samplefeature weighted possibilistic fuzzy kernel clustering algorithm can reduce the impact of noisy data and exceptional points and it has better clustering rate than classic clustering algorithm.

Key words: sample weighted;feature weighted;fuzzy C-means;possibilistic fuzzy clustering;kernel