Possibilistic Fuzzy Clustering Algorithm Based on Sample Weighted

Clustering has been used widely in pattern recognition, image processing, data mining and so on. Many clustering algorithms are sensitive to outlier faults in noisy environments. In this paper, we propose a new algorithm called sample weighted possibilistic fuzzy c-means clustering (SWPFCM). Based on combination sample weighting and a suitable for noise environment of initialization clustering center method, SWPFCM is less sensitive to outliers. The experimental results with data sets show that our proposed algorithm can deal with the amount of noise date, and produce less clustering time and better clustering accuracy.