文章基本信息

标题：A Novel And Improved Technique For Clustering Uncertain Data
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作者：Vandana Dubey ; Mrs A A Nikose
期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2015
卷号：4
期号：1
页码：10148-10151
出版社：IJECS
摘要：Clustering on uncertain data, one of the essential tasks in data mining. The traditional algorithms like K-Meansclustering, UK Means clustering, density based clustering etc, to cluster uncertain data are limited to using geometric distancebased similarity measures and cannot capture the difference between uncertain data with their distributions. Such methods cannothandle uncertain objects that are geometrically indistinguishable, such as products with the same mean but very differentvariances in customer ratings. In the case of K medoid clustering of uncertain data on the basis of their KL divergence similarity,they cluster the data based on their probability distribution similarity. Several methods have been proposed for the clustering ofuncertain data. Some of these methods are reviewed.Compared to the traditional clustering methods, K-Medoid clusteringalgorithm based on KL divergence similarity is more efficient
关键词：Uncertain data clustering; Probability distribution; KL divergence; Initial medoid