Frequent itemset mining has been studied extensively in literature. Most previous studies require the specification of a min/spl I.bar/support threshold and aim at mining a complete set of frequent itemsets satisfying min/spl I.bar/support. However, in practice, it is difficult for users to provide an appropriate min/spl I.bar/support threshold. In addition, a complete set of frequent itemsets is much less compact than a set of frequent closed itemsets. In this paper, we propose an alternative mining task: mining top-k frequent closed itemsets of length no less than min/spl I.bar/l, where k is the desired number of frequent closed itemsets to be mined, and min/spl I.bar/l is the minimal length of each itemset. An efficient algorithm, called TFP, is developed for mining such itemsets without mins/spl I.bar/support. Starting at min/spl I.bar/support = 0 and by making use of the length constraint and the properties of top-k frequent closed itemsets, min/spl I.bar/support can be raised effectively and FP-Tree can be pruned dynamically both during and after the construction of the tree using our two proposed methods: the closed node count and descendant/spl I.bar/sum. Moreover, mining is further speeded up by employing a top-down and bottom-up combined FP-Tree traversing strategy, a set of search space pruning methods, a fast 2-level hash-indexed result tree, and a novel closed itemset verification scheme. Our extensive performance study shows that TFP has high performance and linear scalability in terms of the database size.

Sequential pattern mining has been studied extensively in data mining community. Most previous studies require the specification of a minimum support threshold to perform the mining. However, it is difficult for users to provide an appropriate threshold in practice. To overcome this difficulty, we propose an alternative task: mining top-k frequent closed sequential patterns of length no less than min-l, where k is the desired number of closed sequential patterns to be mined, and minl, is the minimum length of each pattern. We mine closed patterns since they are compact representations of frequent patterns. We developed an efficient algorithm, called TSP, which makes use of the length constraint and the properties of top-k closed sequential patterns to perform dynamic support-raising and projected database-pruning. Our extensive performance study shows that TSP outperforms the closed sequential pattern mining algorithm even when the latter is running with the best tuned minimum support threshold.

In this paper, we propose a new mining task: mining top-k frequent closed patterns of length no less than min_/spl lscr/, where k is the desired number of frequent closed patterns to be mined, and min_/spl lscr/ 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 effectively 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.