One of the most well-known and extended data mining techniques is that of association rule mining, a helpful tool to discover relations between items present in sets of transactions. Nevertheless, in some other scenarios, another interesting issue is that of considering not only the possible relations involving presence of items, but the absence of them. The problem gets more complex when it is...

Previous approaches for mining association rules generate large sets of association rules. Such sets are difficult for users to understand and manage. Here, the concept of a restricted conditional probability distribution is used to explain an association rule. Based on this concept, a new type of association rules, called basic association rules, is defined. We propose the GenBR algorithm to g...

The last decade has witnessed tremendous advances in data mining. We take a retrospective look at these developments, focusing on association rules discovery, and discuss the challenges and opportunities ahead. The full version of the paper will be made available at http://www.almaden.ibm. com/software/quest. Permission to copy without fee all or part of this material is granted provided that t...

This paper surveys some genetic-fuzzy data mining techniques for mining both membership functions and fuzzy association rules. The motivation from crisp mining to fuzzy mining will be first described. Three types of genetic-fuzzy data mining approaches are then described according to the utilized methods and different mining problems, including Integrated GeneticFuzzy approaches for items with ...

Mining association rules is a fundamental data mining task. However, depending on the choice of the parameters (the minimum confidence and minimum support), current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. This is a serious problem because in practice users have limited resources for analyzing t...

In this article, we systematically, deeply and comprehensively analyzed and studied the association rule data mining technology, and induced, analyzed and researched the typical mining algorithms of association rule and their basic principles, and objectively compare the differences among various algorithms. We used to correlation to measure the relations among item sets, and gave the computati...

Business intelligence based on data mining has been one of the popular and indispensable tools for identifying business opportunity in sales and marketing of new products. The traditional data mining methods based on association rules may be inadequate in completely uncovering the hidden patterns of sales based on transaction records. This paper presents a qualitative correlation coefficient mi...

One of the most well-studied problems in data mining is mining for association rules. There was also research that introduced association rule mining methods to conduct classification tasks. These classification methods, based on association rule mining, could be applied for customer segmentation. Currently, most of the association rule mining methods are based on a supportconfidence structure,...

Mining generalized association rules among items in the presence of taxonomy has been recognized as an important model in data mining. Earlier work on generalized association rules confined the minimum supports to be uniformly specified for all items or items within the same taxonomy level. This constraint would restrain an expert from discovering more interesting but much less supported associ...

The implementation of early intervention has close relation to the growth development of developmentally-delayed children. The earlier the intervention is involved the more significant effects and results it will bring to the benefits of these young children. However, providing early intervention exclusively without finding out the relationship between the two would eventually leave the problem...