增量式模糊C有序均值聚类算法

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.

Incremental Fuzzy C-Ordered Means Clustering

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 single-pass 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.