文章基本信息

标题：High Dimensional Data used in Consensus Neighbour Clustering with Fuzzy Based K-Means and Kernel Mapping
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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：11
DOI：10.15680/IJIRCCE.2015.0311060
出版社：S&S Publications
摘要：Clustering is the application of data mining techniques to discover patterns from the datasets. Thisresearch entitled “fuzzy based k-means and kernel mappings with consensus neighbouring clustering in highdimensional data” incorporates clustering concept, which is the process of deriving the information the similarity fromthe unsupervised dataset. Finding the outlier data points that are similar to a training data is challenging task in currenttrend. To discover the cluster ensemble or clustering aggregation, have more frequent change in the similarityinformation, which involves raw data points linked to one another and elimination of outlier information. This researchpresents a framework for discovering data membership from unsupervised high dimensional datasets. By aligning thesimilar groups from the datasets and by using distance sequence or its weighting of match, the similarities between thedata points are determined.
关键词：K-means Algorithm; consensus clustering; kernel mapping; and consensus neighbour clustering.