Exploiting Data Mining Techniques for Compressing Table Constraints

Soufia Bennai , Kamal Amroun , Samir Loudni (1)

1 Equipe CODAG - Laboratoire GREYC - UMR6072

In this paper, we propose an improvement of the compression step of sliced table method proposed by Gharbi et al. [1] for compressing and solving table constraints. We consider only n-ary CSP defined in extensional form. More precisely, we propose to use the cover of an itemset in the FP-tree instead of its frequency to improve the construction step of the resulting compressed tables. Moreover, we propose to exploit the compression rate metric instead of savings to compute frequent itemsets relevant for compression. This allows higher compression and leads to an efficient resolution of compressed tables by identifying more accurate frequent itemsets necessary for compression. Experimental results show the effectiveness and efficiency of our approach.

Keywords: Constraint Satisfaction Problem CSP; Table Constraints; Compressing Table Constraints; Data mining DM

Domains: Computer Science [cs]; Artificial Intelligence [cs.AI]

Soufia Bennai, Kamal Amroun, Samir Loudni. Exploiting Data Mining Techniques for Compressing Table Constraints. IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), Nov 2019, Portland, Oregon, United States. ⟨10.1109/ICTAI.2019.00015⟩. ⟨hal-02463468⟩