增量式模糊C有序均值聚类算法

Incremental Fuzzy C-Ordered Means Clustering

针对传统聚类算法难以处理大规模数据和对噪声数据敏感等问题,基于模糊C有序均值聚类算法（FCOM）,结合single-pass和online增量架构,分别提出了single-pass模糊C有序均值聚类算法（SPFCOM）和online模糊C有序均值聚类算法（OFCOM）.SPFCOM和OFCOM算法首先对FCOM算法加权,然后以数据块为单位对数据集合进行增量式处理.实验结果表明,相较于对比算法,SPFCOM和OFCOM算法在聚类准确率方面得到了提高,还具有更强的鲁棒性.

Because traditional clustering algorithms are difficult to deal with large-scale data and sensitive to noise data,based on the Fuzzy C-ordered-means clustering（ FCOM） algorithm,we propose a singlepass fuzzy C-ordered clustering algorithm,named SPFCOM,and an online fuzzy C-ordered clustering algorithm,named OFCOM,by combining single-pass and online incremental architectures respectively.These two algorithms weight the FCOM algorithm,and incrementally process the large-scale data chunk by chunk. Experimental results show that,compared with other similar prominent algorithms,the SPFCOM and OFCOM algorithms can achieve higher accuracy and better robustness.