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Title |
A Survey of Clustering Uncertain Data Based Probability Distribution Similarity |
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Author |
S.Geetha, E. Mary shyla,MCA |
| Citation |
Vol. 14 No. 9 pp. 77-81 |
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Abstract |
Clustering is one of the major tasks in the field of data mining .The main aim of the clustering is grouping the data or similar objects into one group based on their data find the similarity between the objects. Clustering of uncertain data have been becoming the major issues in the mining uncertain data for data mining or applications. Clustering uncertain data problems have been solved in many ways with the help of data mining techniques or algorithm. In recent work many data mining algorithms solve the issues of the uncertain data object. Generally uncertain data objects can be solved in two ways: measuring the similarity between the data objects or clustered data, measuring the similarity with data objects with Probability Distribution functions. Measuring the similarity between the data objects is based on a similarity distance measure and further clustered with density based clustering or hierarchical clustering methods. In recent years, a numeral of indirect data gathering methodologies has led to the propagation of uncertain data and developing efficient clustering methods. In recent work several datamining methods model uncertain data object. In this work, the uncertain data object has been represented by probability distribution similarity function. Generally the problem of uncertain data objects according to probability distribution happens in many ways. First the probability distribution method for model uncertain data object then after that measure the similarity between data objects using distance metrics, then finally best clustering methods such as partition clustering, density based clustering. This study focus on partition based clustering methods .The survey discusses different methodologies to process and mine uncertain data in a diversity of forms |
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Keywords |
Clustering, Clustering uncertain data, Mining methods and algorithms, density based clustering, partition clustering |
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