Rough K-means Algorithm Based on the Boundary Object Difference Metric
22 Oct 2021-pp 309-318
TL;DR: Zhang et al. as discussed by the authors proposed a new rough k-means algorithm to measure the weight of boundary objects, which considers the distance from boundary objects to their neighbor points and the number of neighbor points together to dynamically calculate the weights of boundary object to clusters that may belong to.
Abstract: Rough k-means algorithm can effectively deal with the problem of the fuzzy boundaries. But traditional rough k-means algorithm set unified weight for boundary object, ignoring the differences between individual objects. Membership degree method of rough fuzzy k-means algorithm is used to measure the membership degree of boundary object to the clusters that they may belong to, ignoring the distribution of neighbor points of the boundary object. So, according to the distribution of neighbor points of the boundary object, we put forward a new rough k-means algorithm to measure the weight of boundary objects. The proposed algorithm considers the distance from boundary objects to their neighbor points and the number of neighbor points of boundary objects together to dynamically calculate the weights of boundary object to clusters that may belong to. Simulation and experiment, through examples verify the effectiveness of the proposed method.
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10,702 citations
TL;DR: Clustering algorithms for data sets appearing in statistics, computer science, and machine learning are surveyed, and their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts are illustrated.
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