文章基本信息

标题：An Enhanced Spectral Clustering for Overlapping Data in Multiple Task Clustering
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作者：R. Renukadevi ; Dr. S. Meenakshi
期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2016
卷号：5
期号：12
页码：20736
DOI：10.15680/IJIRSET.2016.0512109
出版社：S&S Publications
摘要：Clustering is one of the most widely used approaches for exploratory data analysis in data mining. Inlarge scale data sources, multitask clustering is an important research work to handle overlapping data, negative andnon-negative values among clustering of multiple tasks which is used to improve the learning relationship amongrelated tasks and sharing of information across the tasks. Recent past spectral clustering has become the well acceptedclustering algorithm to perform multitask clustering which relies on the eigenstructure of a similarity matrix andoutperforms partitioning of data with more complicated structures than traditional clustering algorithms such as the Kmeansand Fuzzy C-means. This research work proposes an enhanced multitask spectral clustering (EMTSC) techniqueto perform multitask clustering without overlapping in data points among clustering of multiple tasks in large data setsfor improving clustering performance.
关键词：Clustering; multitask clustering; data overlapping; spectral clustering algorithm; clustering;performance