ArticleAuthors: Le, Hoang Son; Nguyen, Dang Tien (2017)
Data are getting larger, and most of them are necessary for our businesses. Rapid explosion of data brings us a number of challenges relating to its complexity and how the most important knowledge can be captured in reasonable time. Fuzzy C-means (FCM)—one of the most efficient
clustering algorithms which have been widely used in pattern recognition, data compression, image segmentation, computer vision and many other fields—also faces the problem of
processing large datasets. In this paper, we propose some novel hybrid clustering algorithms based on incremental clustering and initial selection to tune up FCM for the Big Data problem. The first algorithm determines meshes of rectang... 
