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
