Incremental Beta Distribution Weighted Fuzzy C-Ordered Means Clustering

Streaming data is becoming more and more common in the field of big data and incremental frameworks can address its complexity. The BDFCOM algorithm achieves good results on common form datasets by introducing the ordering mechanism of beta distribution weighting. In this paper, based on the BDFCOM algorithm, two incremental beta distribution weighted fuzzy C-ordered means clustering algorithms, SPBDFCOM and OBDFCOM, are proposed by combining the two incremental frameworks of Single-Pass and Online, respectively. In order to validate the performance of SPBDFCOM and OBDFCOM, this paper selects seven real datasets for experiments and compares their performance with six other incremental clustering algorithms using six evaluation metrics. The results show that the two proposed incremental algorithms perform significantly better compared to other algorithms.