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Volume & Issue no: Volume 3, Issue 8, August 2014

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Title:
Clustering Multi - Attribute Uncertain Data Using Jenson-Shannon Divergence
Author Name:
Mr. V.V. Kulkarni , Prof. V.V.Bag
Abstract:
ABSTRACTS Clustering uncertain data is one of the essential tasks in mining uncertain data. Uncertain data contains the notion of probabilityand it is typically found in the area of sensor networks, weather data, customer rating data etc. The earlier methods for clustering uncertain data also use probability distribution as a similarity measure to cluster uncertain objects.In this paper, uncertain object in discrete domain is modeled, where uncertain object is treated as a discrete random variable. The probability distribution of uncertain object is calculated based on probability mass function. The Jenson-Shannon divergence is used to measure the similarity between two uncertain objects.The partitioning and density based clustering approaches are used to evaluate the performance of Jenson-Shannon Divergence. Experiments are performed to verify the effectiveness and efficiency of model developed and results are at par with the existing approaches. Keywords:Clustering, Discrete Domain, Multi-Attribute Data Uncertain Data.
Cite this article:
Mr. V.V. Kulkarni , Prof. V.V.Bag , " Clustering Multi - Attribute Uncertain Data Using Jenson-Shannon Divergence " , International Journal of Application or Innovation in Engineering & Management (IJAIEM) , Volume 3, Issue 8, August 2014 , pp. 125-131 , ISSN 2319 - 4847.
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