Cluster Center Initialization Parallel Algorithm for K-Means Algorithm

Bo Yan; Ye Zhang; Hong Yi Su; Hong Zheng

doi:10.4028/www.scientific.net/AMR.989-994.2169

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994Cluster Center Initialization Parallel Algorithm...

Cluster Center Initialization Parallel Algorithm for K-Means Algorithm

Abstract:

K-Means algorithm is a one of the most famous unsupervised clustering algorithm. It has many disadvantages, such as sensitivity to the initial clustering centers and computes all the data points multiple times when facing the increasing data volume. In order to overcome the above limitations, this paper proposes to make use of density idea to find k cluster centers by adjusting the threshold. Finally, we design and implementation of the K-Means algorithm on the modern Graphic Processing Unit (GPU). The ratio of distance between classes to distance within classes and speedup are used as evaluation criteria. The experiments indicate that the proposed algorithm significantly improves the stability and efficiency of K-Means algorithm.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

2169-2172

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.2169

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Online since:

July 2014

Authors:

Bo Yan*, Ye Zhang, Hong Yi Su, Hong Zheng

Keywords:

Density, GPU, K-Mean, Parallel

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* - Corresponding Author

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