文章基本信息

标题：Fuzzy Logic Based Consent Neighbor Clustering In High Dimensional Data
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作者：M.Mohanapriya ; Dr. Antony Selvadoss Thanamani
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：8
DOI：10.15680/IJIRCCE.2015. 0308043
出版社：S&S Publications
摘要：High dimensional data clustering arises naturally in a lot of domains, and have regularly presented agreat deal with for usual data mining techniques. In this paper, presents an optimal perspective on the problem ofConsensus Clustering in high-dimensional data. The proposed method called ―Fuzzy based and kernel mappings withConsensus Neighboring clustering (FKCNC)‖, which takes as key measures of correspondence between pairs of datapoints. The proposed method is to establish a unified framework for FKCNC on both supervised and supervised datasets. Also, we examine some important factors, such as the clustering quality and assortment of basic partitioning,which may affect the performances of FKCNC. Experimental results on various synthetic and real world data setsdemonstrate that FKCNC is highly efficient and is equivalent to the state-of-the-art methods in terms of clusteringindex quality. In addition, FKCNC shows high robustness to incomplete basic partitioning with many anomaly values.
关键词：Fuzzy logic; Consensus Clustering; High dimensionality; kernel mapping; Distance function