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  • 标题:PERFORMANCE IMPROVEMENT OF DISK BASED K-MEANS OVER K-MEANS ON LARGE DATASETS
  • 本地全文:下载
  • 作者:SWAGATIKA DEVI ; TRILOKNATH PANDEY ; ALOK KUMAR JAGADEV
  • 期刊名称:Journal of Theoretical and Applied Information Technology
  • 印刷版ISSN:1992-8645
  • 电子版ISSN:1817-3195
  • 出版年度:2013
  • 卷号:49
  • 期号:3
  • 出版社:Journal of Theoretical and Applied
  • 摘要:Clustering is a popular necessity having extensive scope for varied applications. We apply the k-means task in a situation where the volume of data is large and puts pressure on the access memory. The objective is to use less memory and access data sequentially. This paper proposes a method of making the algorithm more effective and efficient; so as to get better clustering with reduced complexity. Our algorithm is based on recent theoretical results, with significant improvements to make it application friendly. Our approach sufficiently simplifies a recently developed algorithm, both in design and analysis. We prove that our algorithm compares favorably with existing algorithms - both theoretically and experimentally, thus providing state-of-the-art performance. Also these algorithms are tested on two datasets and the result is simulated
  • 关键词:Clustering; complexity; K-means; Sequential access.
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