The rationale behind mining frequent itemsets is that only itemsets with high frequency are of interest to users. However, the practical usefulness of frequent itemsets is limited by the significance of the discovered itemsets. A frequent itemset only reflects the statistical correlation between items, and it does not reflect the semantic significance of the items. In this paper, we propose a u...

Data Mining can be defined as an activity that extracts some new nontrivial information contained in large databases. Traditional data mining techniques have focused largely on detecting the statistical correlations between the items that are more frequent in the transaction databases. Also termed as frequent itemset mining , these techniques were based on the rationale that itemsets which appe...

Itemset share has been proposed as a measure of the importance of itemsets for mining association rules. The value of the itemset share can provide useful information such as total profit or total customer purchased quantity associated with an itemset in database. The discovery of share-frequent itemsets does not have the downward closure property. Existing algorithms for discovering share-freq...

Association rule mining is the process of discovering relationships among the data items in large database. It is one of the most important problems in the field of data mining. Finding frequent itemsets is one of the most computationally expensive tasks in association rule mining. The classical frequent itemset mining approaches mine the frequent itemsets from the database where presence of an...

ABSTRACT For a transaction database, a frequent itemset is an itemset included in at least a specified number of transactions. To find all the frequent itemsets, the heaviest task is the computation of frequency of each candidate itemset. In the previous studies, there are roughly three data structures and algorithms for the computation: bitmap, prefix tree, and array lists. Each of these has i...

In this paper, we systematically explore an itemset-based extension approach for generating candidate sequence which contributes to a better and more straightforward search space traversal performance than traditional item-based extension approach. Based on this candidate generation approach, we present FINDER, a novel algorithm for discovering the set of all frequent sequences. FINDER is compo...

Frequent Itemset Mining is a well-known concept in data sciences. If we feed frequent itemset miner algorithms with large datasets they become resource hungry fast as their search space explodes. This problem is even more apparent when we try to use them on Big Data. Recent advances in parallel programming provides good solutions to deal with large datasets but they present their own problems w...