The International Journal of Innovative Research in Computer and Communication Engineering
Clustering on uncertain data, one of the essential tasks in mining uncertain data, posts significant challenges on both modeling similarity between uncertain objects and developing efficient computational methods. The existing methods extend traditional partitioning clustering methods like k-means and density-based clustering methods like DBSCAN and Kullback-Leibler to uncertain data, thus rely on numerical distances between objects. The authors study the problem of clustering data objects whose locations are uncertain. A data object is represented by an uncertainty region over which a probability density function (pdf) is defined.