Constraint-based pattern mining is at the core of numerous data mining tasks. Usually, pattern mining problems are handled as constraint-solving tasks. However, in practice, the resulting patterns are often large, and selecting appropriate thresholds for these constraints can be challenging. This paper addresses these issues by expanding data mining algorithms into the realm of optimization, treating the task as one of identifying the best patterns according to a set of objective functions. We explore a multi-objective optimization approach, where several potentially conflicting objectives are optimized simultaneously. In the absence of preferences, Pareto dominance is commonly used to identify optimal compromise solutions. We present a novel Constraint Programming model designed to efficiently mine Pareto optimal patterns. Our model leverages condensed pattern representations to reduce computational effort and introduces a new global constraint to enforce the closure of patterns across multiple measures. We demonstrate the application of our approach to derive high-quality frequent and (rare) non-redundant association rules without relying on threshold values. The effectiveness of our approach is validated using both UCI datasets and a case study involving gene expression analysis, integrating multiple external gene annotations.

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.

Cryptographic protocols form the backbone of digital society. They are concurrent multiparty communication protocols that use cryptography to achieve security goals such as confidentiality, authenticity, integrity, etc., in the presence of adversaries. Unfortunately, protocol verification still represents a critical task and a major cost to engineer attack-free security protocols. Model checking and SAT-based techniques proved quite effective in this context. This article proposes an efficient automatic model checking approach that exemplifies a security property violation. In this approach, a protocol verification is abstracted as a compact planning problem, which is efficiently solved by a state-of-the-art SAT solver. The experiments performed on some real-world cryptographic protocols succeeded in detecting new logical attacks, violating some security properties. Those attacks encompass both “type flaw” and “replay” attacks, which are difficult to tackle with the existing planning-based approaches.

Fault localization problem is one of the most difficult processes in software debugging. The current constraint-based approaches draw strength from declarative data mining and allow to consider the dependencies between statements with the notion of patterns. Tackling large faulty programs is clearly a challenging issue for Constraint Programming (CP) approaches. Programs with multiple faults raise numerous issues due to complex dependencies between faults, making the localization quite complex for all of the current localization approaches. In this paper, we provide a new CP model with a global constraint to speed-up the resolution and we improve the localization to be able to tackle multiple faults. Finally, we give an experimental evaluation that shows that our approach improves on CP and standard approaches.