文章基本信息

标题：A New Hybridized K-Means Clustering Based Outlier Detection Technique For Effective Data Mining
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作者：H.S.Behera ; Abhishek Ghosh ; Sipak ku. Mishra 等
期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2012
卷号：2
期号：4
出版社：S.S. Mishra
摘要：Nowadays million of databases have been used in business management, Govt., scientific engineering & in many other application & keeps growing rapidly in present day scenario. The explosive growth in data & database has generated an urgent need to develop new technique to remove outliers for effective data mining. In this paper we have suggested a clustering based outlier detection algorithm for effective data mining which uses k-means clustering algorithm to cluster the data sets and outlier finding technique (OFT) to find out outlier on the basis of density based and distance based outlier finding technique.
关键词：K-means clustering algorithm. Density based outlier detection; distance based outlier detection; Outlier Detection Technique ;(OFT).