• Chinapaga Ravi
• M Bal Raju
• N Subhash Chandra

Abstract: In order to enhance the precision in association Rule mining on extension is proposed capture the uncertain item relationships in the data sets. Two sources of uncertainty considered: First one is degree of individual item importance, and second one is the degree of association among the items (Inter- relationships). Generating associations on the probabilistic itemsets. Contrastive Rule Mining (CRM Algorithm) Algorithm On probabilistic Database. This is an efficient algorithm for find strong association rules on Probabilistic itemsets. Previous association rule mining techniques on uncertain data base such as U-Apriori takes more time than the CRM Algorithm. The algorithm shows the significant improved results and experiments of proposed technique.

Uncertain data, probabilistic data, weighted itemsets.

"Contrastive Rule for Generating Associations from Probabilistic Data base", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.170-174, June 2019, Available :http://www.jetir.org/papers/JETIR1908178.pdf

"Contrastive Rule for Generating Associations from Probabilistic Data base", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp170-174, June 2019, Available at : http://www.jetir.org/papers/JETIR1908178.pdf