文章基本信息

标题：Fast Efficient Clustering Algorithm for Balanced Data
本地全文：下载
作者：Adel A. Sewisy ; M. H. Marghny ; Rasha M. Abd ElAziz 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2014
卷号：5
期号：6
DOI：10.14569/IJACSA.2014.050619
出版社：Science and Information Society (SAI)
摘要：The Cluster analysis is a major technique for statistical analysis, machine learning, pattern recognition, data mining, image analysis and bioinformatics. K-means algorithm is one of the most important clustering algorithms. However, the k-means algorithm needs a large amount of computational time for handling large data sets. In this paper, we developed more efficient clustering algorithm to overcome this deficiency named Fast Balanced k-means (FBK-means). This algorithm is not only yields the best clustering results as in the k-means algorithm but also requires less computational time. The algorithm is working well in the case of balanced data.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; Clustering; K-means algorithm; Bee algorithm; GA algorithm; FBK-means algorithm