期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2014
卷号：3
期号：10
页码：8937-8941
出版社：IJECS
摘要：Cluster analysis is one of the prominent unsupervised learning techniques widely used to categorize the data items based on theirsimilarity. Mainly off-line and online analysis through clusters is more attractive area of research. But, high dimensional big data analysis isalways introducing a new dimension in the area of data mining. We have different variable selection methods for clustering of data likedensity based, model based and criterion based variable selection methods. Because high dimensional cluster analysis is giving less accurateresults and high processing time when considering maximum dimensions. To overcome these issues dimensionality reduction techniqueshave been introduced. Here, a million dollar questions are, which dimensions are to be considered? , what type of measures have to beintroduced? And how to evaluate the cluster quality based on those dimensions and measures? Proposed approach effectively answers thesequestions by introducing Ensemble feature subset selection measure along with Extend leader follower algorithm to justify the proposal withexperimental evaluations.
