The task of mining association rules consists of two main steps. The first involves finding the set of all frequent itemsets. The second step involves testing and generating all high confidence rules among itemsets. In this paper we show that it is not necessary to mine all frequent itemsets in the first step, instead it is sufficient to mine the set of closed frequent itemsets, which is much s...

Abstract-Association rule mining is one of the important data mining techniques. It finds correlations among attributes in huge dataset. Those correlations are used to improve the strategy of the future business. The core process of association rule mining is to find the frequent patterns (itemsets) in huge dataset. Countless algorithms are available in the literature to find the frequent items...

Association mining may often derive an undesirably large set of frequent itemsets and association rules. Recent studies have proposed an interesting alternative: mining frequent closed itemsets and their corresponding rules, which has the same power as association mining but substantially reduces the number of rules to be presented. In this paper, we propose an eecient algorithm, CLOSET, for mi...

A distributed rule mining algorithm must minimize the communication cost to reduce the communication bandwidth use and to improve the scalability. There are a few distributed rule mining algorithms reported in the literature. In this paper we propose a new distributed association rule mining algorithm, called DARCI, which reduces the communication cost by up to 40% of the best known algorithm. ...

Association rule mining is the process of finding some relations among the attributes/attribute values of huge database based on support value. Most existing association mining techniques are developed to generate frequent rules based on frequent itemsets generated on market basket datasets. A common property of these techniques is that they extract frequent itemsets and prune the infrequent it...

Association rules mining from temporal dataset is to find associations between items that hold within certain time frame but not throughout the dataset. This problem involves first discovering frequent itemsets which are frequent at certain time intervals and then extracting association rules from such frequent itemsets. In practice, we may have datasets having imprecise or fuzzy time attribute...