Mining Top-K Frequent Closed Patterns without Minimum Support Jiawei Han, Jianyong Wang, Ying Lu, and Petre Tzvetkov   View Paper (PDF)   Return to Main-Track Regular Papers Abstract In this paper, we propose a new mining task: mining top-k frequent closed patterns of length no less than min_l, where k is the desired number of frequent closed patterns to be mined, and min _l is the minimal length of each pattern. An efficient algorithm, called TFP, is developed for mining such patterns without minimum support. Two methods, closed_node_count and descendant_sum are proposed to effiectively raise support threshold and prune FP-tree both during and after the construction of FP-tree. During the mining process, a novel top-down and bottom-up combined FP-tree mining strategy is developed to speed-up support- raising and closed frequent pattern discovering. In addition, a fast hash-based closed pattern verification scheme has been employed to check efficiently if a potential closed pattern is really closed. Our performance study shows that in most cases, TFP outperforms CLOSET and CHARM, two efficient frequent closed pattern mining algorithms, even when both are running with the best tuned min_support. Furthermore, the method can be extended to generate association rules and to incorporate user-specified constraints. Thus we conclude that for frequent pattern mining, mining top-k frequent closed patterns without min support is more preferable than the traditional min_support-based mining. DiSC'03 © 2003 Association for Computing Machinery