Threshold-free Pattern Mining Meets Multi-Objective Optimization: Application to Association Rules

Constraint-based pattern mining is at the core of numerous data mining tasks. Unfortunately, thresholds which are involved in these constraints cannot be easily chosen. This paper investigates a Multi-objective Optimization approach where several (often conflicting) functions need to be optimized at the same time. We introduce a new model for efficiently mining Pareto optimal patterns with constraint programming. Our model exploits condensed pattern representations to reduce the mining effort. To this end, we design a new global constraint for ensuring the closeness of patterns over a set of measures. We show how our approach can be applied to derive high-quality non redundant association rules without the use of thresholds whose added-value is studied on both UCI datasets and case study related to the analysis of genes expression data integrating multiple external genes annotations.

Authors

• Charles Vernerey
• Samir Loudni University of Caen
• Noureddine Aribi
• Yahia Lebbah Olivier Lhomme

Keywords

• Constraint Satisfaction and Optimization: Constraint Optimization
• Constraint Satisfaction and Optimization: Constraint Programming
• Constraint Satisfaction and Optimization: Constraints and Machine Learning
• Data Mining: Exploratory Data Mining
• Data Mining: Frequent Pattern Mining