Issue No. 08 - August (2007 vol. 19)
Previous studies have presented convincing arguments that a frequent pattern mining algorithm should not mine all frequent patterns but only the closed ones because the latter leads to not only a more compact yet complete result set but also better efficiency. However, most of the previously developed closed pattern mining algorithms work under the candidate maintenance-and- test paradigm, which is inherently costly in both runtime and space usage when the support threshold is low or the patterns become long. In this paper, we present BIDE, an efficient algorithm for mining frequent closed sequences without candidate maintenance. It adopts a novel sequence closure checking scheme called Bl-Directional Extension and prunes the search space more deeply compared to the previous algorithms by using the BackScan pruning method. A thorough performance study with both sparse and dense, real, and synthetic data sets has demonstrated that BIDE significantly outperforms the previous algorithm: It consumes an order(s) of magnitude less memory and can be more than an order of magnitude faster. It is also linearly scalable in terms of database size.
data mining, database management systems,frequent closed sequence mining, frequent pattern mining algorithm, BIDE, sequence closure checking scheme, bi-directional extension, BackScan pruning method, database size, data mining,Data mining, Bidirectional control, Itemsets, Runtime, Spatial databases, Application software, Computer bugs, Large-scale systems, Open source software, Association rules,Data mining, frequent closed sequences, BI-Directional Extension.
"Frequent Closed Sequence Mining without Candidate Maintenance", IEEE Transactions on Knowledge & Data Engineering, vol. 19, no. , pp. 1042-1056, August 2007, doi:10.1109/TKDE.2007.1043