SAT‐based and CP‐based declarative approaches for Top‐Rank‐ K  closed frequent itemset mining

What is it about?

This paper presents two declarative approaches to tackle the Top‐Rank‐K Closed FPM problem. The first approach is Boolean Satisfiability‐based (SAT‐based) where we propose an effective encoding for the problem along with an efficient algorithm employing this encoding. The second approach is CP‐based, that is, utilizes Constraint Programming technique, where a simple CP model is exploited in an innovative manner to mine the Top‐Rank‐K Closed FPM itemsets from transactional data sets. Both approaches are evaluated experimentally against other declarative and imperative algorithms. The proposed SAT‐based approach significantly outperforms IM, another SAT‐based approach, and outperforms the proposed CP‐approach for sparse and moderate data sets, whereas the latter excels on dense data sets. An extensive study has been conducted to assess the proposed approaches in terms of their feasibility, performance factors, and practicality of use.

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