activity
20092026
most citedOn SAT Models Enumeration in Itemset Mining

3 citations · 9 across the 10 of their papers we have counts for

collaborators

11 papers

cs.CL2026

Detecting Experiential Intertextuality Across Migration Routes: Beyond Surface Similarity in French Narratives

Sakayo Toadoum Sari, Nelly Robin, Michelle Auzanneau +5

Migrants traversing geographically distinct routes such as the Trans-Saharan and Balkan corridors often recount strikingly parallel lived experiences: police violence, smuggler exp…

cs.AI2026

Enhancing Clustering: An Explainable Approach via Filtered Patterns

Motaz Ben Hassine, Saïd Jabbour

Machine learning has become a central research area, with increasing attention devoted to explainable clustering, also known as conceptual clustering, which is a knowledge-driven u…

cs.AI2019★ 1 cited

Extracting Frequent Gradual Patterns Using Constraints Modeling

Jerry Lonlac, Saïdd Jabbour, Engelbert Mephu Nguifo +2

In this paper, we propose a constraint-based modeling approach for the problem of discovering frequent gradual patterns in a numerical dataset. This SAT-based declarative approach…

cs.AI2018★ 1 cited

Efficient Encodings of Conditional Cardinality Constraints

Abdelhamid Boudane, Said Jabbour, Badran Raddaoui +1

In the encoding of many real-world problems to propositional satisfiability, the cardinality constraint is a recurrent constraint that needs to be managed effectively. Several effi…

cs.AI2015★ 3 cited

On SAT Models Enumeration in Itemset Mining

Said Jabbour, Lakhdar Sais, Yakoub Salhi

Frequent itemset mining is an essential part of data analysis and data mining. Recent works propose interesting SAT-based encodings for the problem of discovering frequent itemsets…

cs.AI2014

On the measure of conflicts: A MUS-Decomposition Based Framework

Said Jabbour, Yue Ma, Badran Raddaoui +2

Measuring inconsistency is viewed as an important issue related to handling inconsistencies. Good measures are supposed to satisfy a set of rational properties. However, defining s…