Paper Details

Clustering is important task in data mining. The main purpose of clustering is grouping the same object datain a huge dataset and finding similarities between the objects. Clustering on unsure data is a most difficult task in bothmodeling similarity between unsure data objects and producing efficient computational method. Clustering uncertaindata problems have been solved by using many different new data mining techniques and various algorithms. Techniqueshave recently been suitable for clustering uncertain data based upon the traditional dividing clustering methods like kmeans and density-based clustering methods like DBSCAN to unsure data, they will determined by geometric distancesbetween objects. Computing the similarity between the data objects will be based upon a similarity distance measure andfurther clustered with occurrence based clustering or hierarchical clustering methods. Such methods cannot handleuncertain items that are geometrically no difference. In the proposed system we could using probability that are essentialcharacteristics of uncertain objects, and are considered in measuring likeness between uncertain objects. The verypopular technique Kullback-Leibler divergence used to procedures the distribution similarity between two uncertain dataitems. First the probability division method for model unsure data object then there after measure the similarity betweendata objects using distance metrics, then finally best clustering methods such as partition clustering, density clustering.

Prof.C.M.Jadhav, Vanashri S.Shinde, “CLUSTERING ON UNCERTAIN DATA BASED PROBABILITY DISTRIBUTION SIMILARITY”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 5, Issue 8, pp. 145-149, August 2018.