Clustering Multi-Attribute Uncertain Data using Probability Distribution

Abstract

Clustering is an unsupervised classification technique for grouping set of abstract objects into classes of similar objects. Clustering uncertain data is one of the essential tasks in mining uncertain data. Uncertain data is typically found in the area of sensor networks, weather data, customer rating data etc. The earlier methods for clustering uncertain data based on probability distribution, uses Kullback-Leibler divergence to measure similarity between two uncertain objects. In this paper, uncertain object in discrete domain is modeled, where uncertain object is treated as a discrete random variable. The JensonShannon divergence is used to measure the similarity between two uncertain objects and integrate it into partitioning and density based clustering approaches. Experiments are performed to verify the effectiveness and efficiency of model developed and results are at par with the existing approaches.