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An efficient method for K-Means clustering

journal contribution
posted on 2023-01-31, 23:08 authored by Z H Huang, Yang Xiang, B Zhang, D Wang, X L Liu
The existing K-Means clustering methods directly act on multidimensional datasets. Hence, these methods are extremely inefficient as the cardinality of input data and the number of clustering attributes increase. Motivated by the above fact, in this paper, an efficient approach for K-Means clustering based on the structure of regular grid, called KMCRG (K-Means Clustering based on Regular Grid), is proposed. This method effectively implements K-Means clustering by taking cell as handling object. Especially, this method uses the tactics of grid weighted iteration to effectively gain the final K classes. The experiment results show that the algorithm can quickly gain the clustering results without losing clustering precision.

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Journal

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence

Volume

23

Pagination

516 - 521

ISSN

1003-6059

Publication classification

CN.1 Other journal article

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