Tingfu Zhou
Wensheng Gan
ABSTRACT
Pattern discovery (aka pattern mining) is a fundamental task in the field of data science. Statistically significant pattern mining (SSPM) is the task of finding useful patterns that statistically occur more often from databases for one class than for another. The existing SSPM task does not consider the weight of each item. While in the real world, the significant level of different items/objects is various. Therefore, in this paper, we introduce the Weighted Statistically Significant Patterns Mining (WSSPM) problem and propose a novel WSSpm algorithm to successfully solve it. We present a new framework that effectively mines weighted statistically significant patterns by combining the weighted upper-bound model and the multiple hypotheses test. We also propose a new weighted support threshold that can satisfy the demand of WSSPM and prove its correctness and completeness. Besides, our weighted support threshold and modified weighted upper-bound can effectively shrink the mining range. Finally, experimental results on several real datasets show that the WSSpm algorithm performs well in terms of execution time and memory storage.
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Index Terms
- Weighted Statistically Significant Pattern Mining
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