An efficient algorithm for finding dense regions for mining quantitative association rules

Many algorithms have been proposed for mining boolean association rules. However, very little work has been done in mining quantitative association rules. Although we can transform quantitative attributes into boolean attributes, this approach is not effective and is difficult to scale up for high-dimensional cases and also may result in many imprecise association rules. Newly designed algorithms for quantitative association rules still are persecuted by the problems of nonscalability and noise. In this paper, an efficient algorithm, DRMiner, is proposed. By using the notion of ''density'' to capture the characteristics of quantitative attributes and an efficient procedure to locate the ''dense regions'', DRMiner not only can solve the problems of previous approaches, but also can scale up well for high-dimensional cases. Evaluations on DRMiner have been performed using synthetic databases. The results show that DRMiner is effective and can scale up quite linearly with the increasing number of attributes.

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