Statistically Significant Discriminative Patterns Searching

Abstract

Discriminative pattern mining is an essential task of data mining. This task aims to discover patterns which occur more frequently in a class than other classes in a class-labeled dataset. This type of patterns is valuable in various domains such as bioinformatics, data classification. In this paper, we propose a novel algorithm, named SSDPS, to discover patterns in two-class datasets. The SSDPS algorithm owes its efficiency to an original enumeration strategy of the patterns, which allows to exploit some degrees of anti-monotonicity on the measures of discriminance and statistical significance. Experimental results demonstrate that the performance of the SSDPS algorithm is better than others. In addition, the number of generated patterns is much less than the number of the other algorithms. Experiment on real data also shows that SSDPS efficiently detects multiple SNPs combinations in genetic data.

Showing Academic Performance Predictions during Term Planning: Effects on Students’ Decisions, Behaviors, and Preferences

“…We observe some recurrent cliques in all scenarios, e.g., the set { 1, 2, 4, 6 } is prominent in almost all cases. Motivated by this insight, we carried out a deeper analysis based on contrast itemset mining [42,55]. This technique finds groups of courses that co-occur more frequently in a visual representation than in others.…”

“…We also carried out a contrast itemset mining analysis [42,55] to investigate whether some prediction representations may have induced students to select particular groups of courses.…”