Boolean satisfiability for sequence mining

Jabbour, Saïd; Saïs, Lakhdar; Salhi, Yakoub

doi:10.1145/2505515.2505577

Cited by 20 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This would require adding rewrite rules for specific encodings of the constraints into SAT/ASP. Other pattern mining settings that have been studied in a CP framework such as sequences with wildcards [47,48,49] and sequential patterns [50] can be expressed in MiningZinc too. The main difference is in the definition of the cover relation, and one could add such relations to the MiningZinc library as reusable functions.…”

Section: Related Workmentioning

confidence: 99%

MiningZinc: A declarative framework for constraint-based mining

Guns¹,

Dries²,

Nijssen³

et al. 2017

Artificial Intelligence

View full text Add to dashboard Cite

We introduce MiningZinc, a declarative framework for constraint-based data mining. MiningZinc consists of two key components: a language component and an execution mechanism.First, the MiningZinc language allows for high-level and natural modeling of mining problems, so that MiningZinc models are similar to the mathematical definitions used in the literature. It is inspired by the Zinc family of languages and systems and supports user-defined constraints and functions.Secondly, the MiningZinc execution mechanism specifies how to compute solutions for the models. It is solver independent and supports both standard constraint solvers and specialized data mining systems. The high-level problem specification is first translated into a normalized constraint language (FlatZinc). Rewrite rules are then used to add redundant constraints or solve subproblems using specialized data mining algorithms or generic constraint programming solvers. Given a model, different execution strategies are automatically extracted that correspond to different sequences of algorithms to run. Optimized data mining algorithms, specialized processing routines and generic solvers can all be automatically combined.Thus, the MiningZinc language allows one to model constraint-based itemset mining problems in a solver independent way, and its execution mechanism can automatically chain different algorithms and solvers. This leads to a unique combination of declarative modeling with high-performance solving.

show abstract

Section: Related Workmentioning

confidence: 99%

MiningZinc: A declarative framework for constraint-based mining

Guns¹,

Dries²,

Nijssen³

et al. 2017

Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…More recently, [9], [24] have proposed a SAT-Based approach for discovering frequent, closed and maximal sequential patterns with wildcards in only a single sequence of items or itemsets. However, unlike [9], [24], our approach considers a sequence database of items. Moreover, our approach allows to consider a broader set of constraints that are not handled in [9] (e.g.…”

Section: Dynamic Csp For Mining Richer Sequence Patternsmentioning

confidence: 99%

“…However, unlike [9], [24], our approach considers a sequence database of items. Moreover, our approach allows to consider a broader set of constraints that are not handled in [9] (e.g. gap, regular expression, top-k and relevant subgroup constraints).…”

Section: Dynamic Csp For Mining Richer Sequence Patternsmentioning

confidence: 99%

“…In fact, studying the behavior of our CP model on datasets having more than 500 sequences becomes interesting as the size of instances are reasonably large: up to 120, 020 variables (with 120, 000 boolean variables), domain size ranging from 2 to 4, 197, and up to 6, 848, 002 constraints. To study the influence of the gap constraint on the extraction process, we considered two cases: with gap (0, 9) and without gap constraint. In the last case, the number of wildcards is limited by the length of patterns.…”

Section: A Mining Sequence Patterns From Biomedical Textsmentioning

confidence: 99%

See 1 more Smart Citation

Mining Relevant Sequence Patterns with CP-Based Framework

Kemmar

Ugarte

Loudni

et al. 2014

2014 IEEE 26th International Conference on Tools With Artificial Intelligence

View full text Add to dashboard Cite

International audience—Sequential pattern mining under various constraints is a challenging data mining task. The paper provides a generic framework based on constraint programming to discover sequence patterns defined by constraints on local patterns (e.g., gap, regular expressions) or constraints on patterns involving combination of local patterns such as relevant subgroups and top-k patterns. This framework enables the user to mine in a declarative way both kinds of patterns. The solving step is done by exploiting the machinery of Constraint Programming. For complex patterns involving combination of local patterns, we improve the mining step by using dynamic CSP. Finally, we present two case studies in biomedical information extraction and stylistic analysis in linguistics

show abstract

“…Examples include unbounded model checking [9], reachability analysis [5], prime implicant generation [11], and so on. In recent years, a data mining framework based on the reduction to an ALL-SAT problem has been proposed [7]. Most ALL-SAT solvers are implemented on top of a SAT solver that searches one solution.…”

Section: Introductionmentioning

confidence: 99%

BDD construction for all solutions SAT and efficient caching mechanism

Toda

Tsuda

2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

View full text Add to dashboard Cite

We improve an existing OBDD-based method of computing all total satisfying assignments of a Boolean formula, where an OBDD means an ordered binary decision diagram that is not necessarily reduced. To do this, we introduce lazy caching and finer caching by effectively using unit propagation. We implement our methods on top of a modern SAT solver, and show by experiments that lazy caching significantly accelerates the original method and finer caching in turn reduces an OBDD size.

show abstract

Boolean satisfiability for sequence mining

Cited by 20 publications

References 27 publications

MiningZinc: A declarative framework for constraint-based mining

MiningZinc: A declarative framework for constraint-based mining

Mining Relevant Sequence Patterns with CP-Based Framework

BDD construction for all solutions SAT and efficient caching mechanism

Contact Info

Product

Resources

About