In this paper, we first focus our attention on the question of how much space remains for performance improvement over current association rule mining algorithms. Our strategy is to compare their performance against an “Oracle algorithm” that knows in advance the identities of all frequent itemsets in the database and only needs to gather their actual supports to complete the mining process. Ou...

In the literature of data mining and statistics, numerous interestingness measures have been proposed to disclose succinct object relationships of association patterns. However, it is still not clear when a measure is truly effective in large data sets. Recent studies have identified a critical property, null-(transaction) invariance, for measuring event associations in large data sets, but man...

Traditional association rule mining based on the support-confidence framework provides the objective measure of the rules that are of interest to users. However, it does not reflect the utility of the rules. To extract non-redundant association rules in support-confidence framework frequent closed itemsets and their generators play an important role. To extract non-redundant association rules a...

In this paper, a review of four different association rule mining algorithmsApriori, AprioriTid,Apriori hybrid and tertius algorithms and their drawbacks which would be helpful to find new solution for the Problems found in these algorithms and also presents a comparison between different association mining algorithms. Association rule mining is the one of the most important technique of the da...

Generalized association rules are rules that contain some background knowledge, therefore, giving a more general view of the domain. This knowledge is codified by a taxonomy set over the data set items. Many researches use taxonomies in different data mining steps to obtain generalized rules. In general, those researches reduce the obtained set by pruning some specialized rules using a subjecti...

Data mining is as a new area of research has taken its place as one of the most important techniques in the decision making process. Mining association rules is one of simple yet powerful technique in the data mining process The problem of mining association rules is composed of finding the large itemsets and to generate the association rules from these itemsets. Usually the dataset must be sca...

The problem of discovering of frequent market baskets and association rules has been considered widely in literatures of data mining. In this study, by using the algebraic representation of market basket model, we propose a concept of logical constraints of items in an effort to detect the logical relationships hidden among them. Via the relationships of the propositional logics and logical con...

In the year 2001, one of the authors of this editorial wrote an article about support versus confidence in the data mining technique, association rules. This article was presented at a conference and never formally published [22]. In the last four years this article has been downloaded nearly twenty-thousand times from an open access repository. This interest by researchers and practitioners ha...

ÐAssociation rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent itemsets and then, forming conditional implication rules among them. In this paper, we present efficient algorithms for the discovery of frequent itemsets which forms the compute intensive phase of the task. The algorithms utili...

Correlated pattern mining has become increasingly important recently as an alternative or an augmentation of association rule mining. Though correlated pattern mining discloses the correlation relationships among data objects and reduces significantly the number of patterns produced by the association mining, it still generates quite a large number of patterns. In this paper, we propose closed ...