1 research outputs found
Discovery of Maximal Frequent Item Sets using Subset Creation
Data mining is the practice to search large amount of data to discover data
patterns. Data mining uses mathematical algorithms to group the data and
evaluate the future events. Association rule is a research area in the field of
knowledge discovery. Many data mining researchers had improved upon the quality
of association rule for business development by incorporating influential
factors like utility, number of items sold and for the mining of association
data patterns. In this paper, we propose an efficient algorithm to find maximal
frequent itemset first. Most of the association rule algorithms used to find
minimal frequent item first, then with the help of minimal frequent itemsets
derive the maximal frequent itemsets, these methods consume more time to find
maximal frequent itemsets. To overcome this problem, we propose a new approach
to find maximal frequent itemset directly using the concepts of subsets. The
proposed method is found to be efficient in finding maximal frequent itemsets