Correlation-based Interesting Association Rules Mining

Recently, our capabilities of both generating and collecting data have been increasing rapidly. The explosive growth in data and database has generated an urgent need for new techniques and tools that can intelligently and automatically transform the processed data into useful information and knowledge. Consequently, data mining has become a research area with increasing importance. Since its introduction, Association Rule Mining, has become one of the core data mining tasks, and has attracted tremendous interest among data mining researchers and practitioners.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. For the first step, computable complexity is the bottleneck of the algorithm for the number of frequent itemsets increases with the number of items exponentially. Fortunately some efficient algorithms have been presented in literatures and mostly can prune huge search space based on the minimal threshold by quality measure of 4he-Fule,-For the second step mentioned above, one of important properties is these mined rules must be interesting to the user. However, association rule mining algorithms tend to produce a huge number of rules, most of which are of no interest to the user.In this paper, we analyzed some problems existed in the association rule mining firstly. Then statistic correlation concept was introduced and based on which the rule interestingness measure was defined What we are interested in during the mining is those rules with strong item correlation. So the interesting measure introduced in this paper severed as a constraint for those independent or negative correlation rules. With it associated with the support and the confidence we can find only interesting or useful rules from data sets. From two aspects, theoretically and intuitively, we showed the rationality of the measure and gave a description of the mining interesting rules algorithm. In the end, an example was given to show the efficiency of the algorithm.Interestingness is a relative and domain specific conception, so the method introduced in this paper is not adaptable for all conditions though it is an objective measure. For example, in certain field positive correlation is considered interesting, but in another background negative correlation is our preference. In other words, interestingness is domain specific. Our discuss proceeded in the background of market basket data so rules with items positive correlated are interesting and useful for market decision.