Data mining is sorting through data to identify patterns and establish relationships. Association rule mining is a well established method of data mining that identifies significant correlations between items in transactional data. Measures like support count, comprehensibility and interestingness, used for evaluating a rule can be thought of as different objectives of association rule mining p...

Data Mining finds hidden pattern in data sets and association between the patterns. To achieve the objective of data mining association rule mining is one of the important techniques. Association rule mining is a particularly well studied field in data mining given its importance as a building block in many data analytics tasks. Many studies have focused on efficiency because the data to be min...

Classification is an important subject in data mining and machine learning, which has been studied extensively and has a wide range of applications. Classification based on association rules, also called associative classification, is a technique that uses association rules to build classifier. CMAR employs a novel data structure, association rule, to compactly store and efficiently retrieve a ...

Association rules are one of the most researched areas of data mining and have recently received much attention from the database community. They have proven to be quite useful in the marketing and retail communities as well as other more diverse fields. In this paper we provide an overview of association rule research.

Discovering association rules between items in a large database of sales transactions has been described as an important database mining problem. Relationships between data are called associations. Association rule mining reveals such interesting relationships. These association rules are presented in a compact form, eliminating redundancy. Elimination of redundancy in the association rule set ...

Association rule mining is one of the most important and well researched techniques of data mining for descriptive task, initially used for market basket analysis. It finds all the rules existing in the transactional database that satisfy some minimum support and minimum confidence constraints. Classification using Association rule mining is another major Predictive analysis technique that aims...

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 ...

Association rule mining is the most important technique in the field of data mining. The main task of association rule mining is to mine association rules by using minimum support thresholds decided by the user, to find the frequent patterns. Above all, most important is research on increment association rules mining. The Apriori algorithm is a classical algorithm in mining association rules. T...

Mining association rules is one of the most important tasks in data mining. The classical model of association rules mining is supportconfidence. The support-confidence model concentrates only on the existence or absence of an item in transaction records and does not take into account the products’ prices and quantities and how such these detailed information can affect the overall performance ...

Association rule mining is a very important research topic in the field of data mining. Discovering frequent itemsets is the key process in association rule mining. Traditional association rule algorithms adopt an iterative method to discovery, which requires very large calculations and a complicated transaction process. Because of this, a new association rule algorithm called ABBM is proposed ...