Clustering Uncertain Data Objects Using Jeffreys-Divergence and Maximum Bipartite Matching Based Similarity Measure

In recent years, uncertain data clustering has become the subject of active research in many fields, for example, pattern recognition, and machine learning. Nowadays, researchers have committed themselves to substitute traditional distance or similarity measures with new metrics existing centralized algorithms order tackle uncertainty data. However, perform clustering, representation plays an imperative role. this paper, a Monte-Carlo integration is adopted modified express probabilistic form. Then three are used determine closeness between two probability distributions including one novel measure. These derived from notion Kullback-Leibler divergence Jeffreys divergence. Finally, density-based spatial applications noise k-medoids implemented on synthetic database real-world databases. The obtained outcomes confirm that proposed technique defeats some algorithms.