Three-Way Clustering Algorithms Based on Disturbances and K-Means
Abstract: K-means algorithm is a traditional algorithm used for partition clustering, and its essence is a hard clustering, that is, the object studied only has two possible results, either belonging to this class or not belonging to this class, and its segmentation results are highly accurate. However, this algorithm has obvious disadvantages, and it is unable to deal with objects with features that are not obvious. The three-way clustering is a kind of fuzzy clustering division, which can deal with the non-obvious objects through the definition of core domain and boundary domain. This paper combines the ideas of three-way decision theory and K-means algorithm to form a new clustering algorithm. It cannot only maintain the original accuracy when clustering, but also make a more reasonable classification of relatively uncertain points. Then the core domain and boundary domain of the cluster are separated by perturbation processing.
文章引用: 沈丹 , 王晓磊 , 王平心 (2018) 基于K-means的三支聚类算法。 应用数学进展， 7， 1349-1356. doi: 10.12677/AAM.2018.710157
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