Weighted Fuzzy Clustering Algorithm Based on Dynamic Interval

Clustering is widely used in data mining and data analysis, and a great many of troubles have been caused by incomplete data in clustering. Aiming at the inaccurate problem of filling missing attributes with estimation method in incomplete data clustering, a weighted fuzzy clustering algorithm is proposed based on dynamic interval. Firstly, the nearest neighbor sample sets of the missing attribute are constructed by the attribute correlation and then the missing attribute interval is formed. To further reduce the interval filling error, the interval factor which is based on the dispersion of the nearest neighbor sample set is used to adjust the interval size. Secondly, in order to fully exploit the implicit information of the attribute space and reduce the influence of the outliers on the cluster center, complete interval datasets are weighted based on local density of samples. Finally, the interval weighted fuzzy clustering is completed by the above improvement. The proposed clustering algorithm is verified by multiple UCI datasets and artificial datasets. The experimental results show that the weighted fuzzy clustering algorithm of dynamic interval can effectively improve the clustering accuracy, robustness and stability of convergence