• M.GEETHA

Frequent itemset mining is the recent research topic in the data mining systems. It generally composes of tremendous volume of frequently searched/retrieved item with low/ high itemset values. This dilemma doesn’t satisfy the user’s requirements. The utility itemsets is an important topic and it can be measure in terms of weight, value, quantity and all other information’s depending on the user’s requirements. If the utility itemset is no less than user specified min utility, so this itemset is called a utility of high itemset. It contains a many applications like biomedicine, mobile computing, market analysis, etc. In database, the HUI is a difficult, because in FIM used the downward closer property is does not hold the utility of itemsets. Superset the low utility itemset can be a high utility so theHUI pruning search space is also difficult. To overcome this issue, we discovered fittest threshold for mining the relevant itemsets from set of itemsets. Setting of min-util value to the user is a daunting task. In order to find an efficient threshold value for the users, the behaviors of the users are studied. In this work, we proposed two mechanisms, namely, mining top k utility itemsets and mining top k utility itemsets in single phase in which k is the number of covered HUI mining. Initially, we give an auxiliary examination of the two calculations with talks on their preferences and restrictions. Exact assessments on both genuine and manufactured datasets demonstrate that the execution of the proposed calculations is near that of the ideal instance of best in class utility mining calculations.

Frequent itemset mining is the recent research topic in the data mining systems. It generally composes of tremendous volume of frequently searched/retrieved item with low/ high itemset values. This dilemma doesn’t satisfy the user’s requirements. The utility itemsets is an important topic and it can be measure in terms of weight, value, quantity and all other information’s depending on the user’s requirements. If the utility itemset is no less than user specified min utility, so this itemset is called a utility of high itemset. It contains a many applications like biomedicine, mobile computing, market analysis, etc. In database, the HUI is a difficult, because in FIM used the downward closer property is does not hold the utility of itemsets. Superset the low utility itemset can be a high utility so theHUI pruning search space is also difficult. To overcome this issue, we discovered fittest threshold for mining the relevant itemsets from set of itemsets. Setting of min-util value to the user is a daunting task. In order to find an efficient threshold value for the users, the behaviors of the users are studied. In this work, we proposed two mechanisms, namely, mining top k utility itemsets and mining top k utility itemsets in single phase in which k is the number of covered HUI mining. Initially, we give an auxiliary examination of the two calculations with talks on their preferences and restrictions. Exact assessments on both genuine and manufactured datasets demonstrate that the execution of the proposed calculations is near that of the ideal instance of best in class utility mining calculations.

"ENHANCEMENT OF K ITEMSETS FROM HIGH UTILITY ITEMSETS USING FASTER HIGH-UTILITY ITEMSET MINER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.39-46, March-2023, Available :http://www.jetir.org/papers/JETIRFV06009.pdf

"ENHANCEMENT OF K ITEMSETS FROM HIGH UTILITY ITEMSETS USING FASTER HIGH-UTILITY ITEMSET MINER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. pp39-46, March-2023, Available at : http://www.jetir.org/papers/JETIRFV06009.pdf