Analysis of Knowledge Set Discovery in Mining Items with Enhanced Apriori Association Algorithm

Gajula Bharathi, Gunna Kishore

Abstract: Frequent item generation is a key approach in association rule mining. The Data mining is the process of generating frequent itemsets that satisfy minimum support. Efficient algorithms to mine frequent patterns are crucial in data mining. Since the Apriori algorithm was proposed to generate the frequent item sets, there have been several methods proposed to improve its performance. But they do not satisfy the time constraint. However, most still adopt its candidate set generation-and-test approach. In addition, many methods do not generate all frequent patterns, making them inadequate to derive association rules. The Enhance apriori algorithm has proposed in this paper requires less time in comparison to apriori algorithm. So the time is reducing.

Keywords: Apriori, Item set, Frequent Item set, Support count, threshold, Confidence