A Sliding-Window Approach to Mining Maximal Large Itemsets for Large Databases

Authors

Ye-In Chang, National Sun Yat-Sen University, TaiwanFollow
Chen-Chang Wu, National Sun Yat-Sen University, TaiwanFollow
Jiun-Rung Chen, National Sun Yat-Sen University, TaiwanFollow
Yuan-Feng Chang, National Sun Yat-Sen University, TaiwanFollow

Document Type

Article

Abstract

In this paper, we propose a Sliding-Window approach, the SWMax algorithm, which could provide good performance for both mining maximal itemsets and incremental mining. Our SWMax algorithm is a two-passes partition-based approach. For incremental mining, if an itemset with size equal to 1 is not large in the original database, it could not be found in the updated database based on the SWF algorithm. Our SWMax algorithm will support incremental mining correctly. From our simulation, the results show that our SWMax algorithm could generate fewer number of candidates and needs less time than the SWF algorithm.

Recommended Citation

Chang, Ye-In; Wu, Chen-Chang; Chen, Jiun-Rung; and Chang, Yuan-Feng, "A Sliding-Window Approach to Mining Maximal Large Itemsets for Large Databases" (2007). ICEB 2007 Proceedings (Taipei, Taiwan). 22.
https://aisel.aisnet.org/iceb2007/22