A Review of Clustering Algorithms for Clustering Uncertain Data

Abstract

Clustering is an important task in the Data Mining. Clustering on uncertain data is a challenging in both modeling similarity between objects of uncertain data and developing efficient computational method. The most of the previous method for clustering uncertain data extends partitioning clustering methods and Density based clustering methods, which are based on geometrical distance between two objects. Such method cannot handle uncertain objects that are cannot distinguishable by using geometric properties and Distribution regarding to object itself is not considered. Probability distribution is an important characteristic is not considered during measuring similarity between two uncertain objects. The well known technique Kullbak-Leibler divergence used to measures the similarity between two uncertain objects. The goal of this paper is to provide detailed review about clustering uncertain data by using different methods & showing effectiveness of each algorithm.