An Algorithm for Mining Closed Frequent K-Itemset

The discovery of association rules in a transaction database is a problem in data mining. Finding frequent itemset is an expensive step and lot of research was focused on it. Unfortunately, the collection of frequent itemsets extracted from a dataset is often very large. This makes the task of analyst hard, since he has to extract useful knowledge from a huge amount of frequent patterns. Closed itemsets are a solution to this problem. A number of algorithms were developed for mining closed frequent itemsets. In analyst point of view, to find a particular itemset is closed frequent itemset or not, none of the algorithms were developed. In this paper, we proposed a new algorithm for mining closed frequent K-itemset (CFI). To find the closed frequent K-itemset, the algorithm starts searching of itemsets whose length is at least K, i.e., the itemsets whose length is less than K will not be considered for further processing which reduces the size and number of comparisons to be performed. It also prunes the K, K+1 … itemsets whose support is less than and greater than of the minimum support value which reduces the processing overhead.