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基于k-means的(1+ε)近似算法求解

(1+ε)-Approximation Algorithm for k-means
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摘要 针对Amit Kumar提出的求解k-means算法的1+ε近似求解随机算法,提出了一个改进措施用来提高每次取样的成功概率。当固定k和ε值时该算法为线性的。通过多次运行该算法能以较高的概率求出k-means算法的1+ε近似值。 This paper introduces an improvement algorithm given by Amit Kumar for the problem of k - means. This improvement algorithm can get a higher probability for success in the process of sampling. This algorithm running time can be regarded as linear when fixed k and ε. After running this algorithm several times,we can get a higher probability to the ratio of (1 +ε)- approximation for the k - means problem.
出处 《现代电子技术》 2006年第19期154-156,共3页 Modern Electronics Technique
关键词 k—means聚类 随机算法 取样 k - means cluster randomized algorithm sample
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参考文献3

  • 1Inaba M,Kaoth N,Imai H.Application of Weighted Voronoi Diagrams and Randomization to Variance-based k-clustering:(Extended Abstract).In Proceeding of the Tenth Annual Symposium on Computational Geometry,1994:332 -339.
  • 2Amit Kumar,Yogish Saharwal,Sandeep Sen.A Sample Linear Time (1+ε) Algorithm for k-Means Clustering in any Dimensions.2004:454-462.
  • 3Deerwester S C,Dumais S T,landauer T K,et al.Indexing by Latent Semantic Analysis[J].Amer J.Soc.Inform,1990,41(6):391-407.

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