文章基本信息

标题：Combination of Density Based and Partition Based Clustering Algorithm-DBK Me
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作者：B.L. Krishna ; P.Jhansi Lakshmi ; P.Satya Prakash 等
期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2012
卷号：3
期号：3
页码：4491-4494
出版社：TechScience Publications
摘要：Mining or extracting the knowledge from the large amount of data is known as data mining. Here, the collection of data increases exponentially so that for extracting the efficient data we need good methods in data mining. Data mining analyzes several methods for extracting the data. Clustering is one of the methods for extracting the data from large amount of data. Multiple clustering algorithms were developed for clustering. Multiple clustering can be combined so that the final partitioning of data provides better clustering. Efficient density based k-means clustering algorithm has been proposed to overcome the drawbacks of dbscan and k-means clustering algorithms. The algorithm performs better than dbscan while handling clusters of circularly distributed data points and slightly overlapped clustering.
关键词：clustering analysis; k-means; and dbscan.R. imag