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	A K-means clustering with optimized initial center based on Hadoop platform
	Lin, Kunhui ; Li, Xiang ; Zhang, Zhongnan ; Chen, Jiahong ; Lin KH(林坤辉) ; Zhang ZN(张仲楠)
	2014
英文摘要	Conference Name:9th International Conference on Computer Science and Education, ICCCSE 2014. Conference Address: Vancouver, BC, Canada. Time:August 22, 2014 - August 24, 2014.; With the explosive growth of data, the traditional clustering algorithms running on separate servers can not meet the demand. To solve the problem, more and more researchers implement the traditional clustering algorithms on the cloud computing platforms, especially for K-means clustering. But, few researchers pay attention to the K-means clustering structure, and most of researchers optimized the model of the cloud computing platform to raise the computing speed of K-means clustering. However the problem of instability caused by the random initial centers still exists. In this paper, we propose a K-means clustering algorithm with optimized initial centers based on data dimensional density. This method avoids the deficiency of the random initial centers and improves the stability of the K-means clustering. The experimental results show that the approach achieves a good performance on K-means, and improves the accuracy of K-means clustering on the test set.
语种	英语
出处	http://dx.doi.org/10.1109/ICCSE.2014.6926466
出版者	Institute of Electrical and Electronics Engineers Inc.
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/85822]
专题	软件学院－会议论文
推荐引用方式 GB/T 7714	Lin, Kunhui,Li, Xiang,Zhang, Zhongnan,et al. A K-means clustering with optimized initial center based on Hadoop platform. 2014-01-01.

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