Mining Relevant Sequence Patterns with CP-Based Framework

Kemmar, Amina; Ugarte, Willy; Loudni, Samir; Charnois, Thierry; Lebbah, Yahia; Boizumault, Patrice; Crémilleux, Bruno

doi:10.1109/ictai.2014.89

Cited by 10 publications

(23 citation statements)

References 21 publications

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“…Each sequence is encoded by an automaton capturing all subsequences that can occur in it. [9] have proposed a CSP model for SPM with wildcards. They show how some constraints dealing with local patterns (e.g.…”

Section: Cp Methods For Spmmentioning

confidence: 99%

PREFIX-PROJECTION Global Constraint for Sequential Pattern Mining

Kemmar

Loudni

Lebbah

et al. 2015

Lecture Notes in Computer Science

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Sequential pattern mining under constraints is a challenging data mining task. Many efficient ad hoc methods have been developed for mining sequential patterns, but they are all suffering from a lack of genericity. Recent works have investigated Constraint Programming (CP) methods, but they are not still effective because of their encoding. In this paper, we propose a global constraint based on the projected databases principle which remedies to this drawback. Experiments show that our approach clearly outperforms CP approaches and competes well with ad hoc methods on large datasets.

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Section: Cp Methods For Spmmentioning

confidence: 99%

PREFIX-PROJECTION Global Constraint for Sequential Pattern Mining

Kemmar

Loudni

Lebbah

et al. 2015

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

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“…For example, CP was used to develop first declarative systems for itemset mining [17] and pattern set mining [24,25]. Recently, declarative approaches have been extended to support sequence mining [26] and graph mining [27].…”

Section: Related Workmentioning

confidence: 99%

Flexible constrained sampling with guarantees for pattern mining

Dzyuba

Leeuwen

Raedt

2017

Data Min Knowl Disc

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Pattern sampling has been proposed as a potential solution to the infamous pattern explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns are sampled proportional to a given quality measure. Several sampling algorithms have been proposed, but each of them has its limitations when it comes to 1) flexibility in terms of quality measures and constraints that can be used, and/or 2) guarantees with respect to sampling accuracy. We therefore present Flexics, the first flexible pattern sampler that supports a broad class of quality measures and constraints, while providing strong guarantees regarding sampling accuracy. To achieve this, we leverage the perspective on pattern mining as a constraint satisfaction problem and build upon the latest advances in sampling solutions in SAT as well as existing pattern mining algorithms. Furthermore, the proposed algorithm is applicable to a variety of pattern languages, which allows us to introduce and tackle the novel task of sampling sets of patterns. We introduce and empirically evaluate two variants of Flexics: 1) a generic variant that addresses the well-known itemset sampling task and the novel pattern set sampling task as well as a wide range of expressive constraints within these tasks, and 2) a specialized variant that exploits existing frequent itemset techniques to achieve substantial speed-ups. Experiments show that Flexics is both accurate and efficient, making it a useful tool for pattern-based data exploration.Comment: Accepted for publication in Data Mining & Knowledge Discovery journal (ECML/PKDD 2017 journal track

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“…Several specialized approaches have been proposed [6,10,18] but they have a lack of genericity to handle simultaneously various types of constraints. Recently, a few proposals [4,8,11,12] have investigated relationships between GSPM and constraint programming (CP) in order to provide a declarative approach, while exploiting efficient and generic solving methods. But, due to the size of the proposed encodings, these CP methods are not as efficient as specialized ones.…”

Section: Introductionmentioning

confidence: 99%

A Global Constraint for Mining Sequential Patterns with GAP Constraint

Kemmar¹,

Loudni²,

Lebbah³

et al. 2016

Integration of AI and OR Techniques in Constraint Programming

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Abstract. Sequential pattern mining (SPM) under gap constraint is a challenging task. Many efficient specialized methods have been developed but they are all suffering from a lack of genericity. The Constraint Programming (CP) approaches are not so effective because of the size of their encodings. In [7], we have proposed the global constraint PREFIX-PROJECTION for SPM which remedies to this drawback. However, this global constraint cannot be directly extended to support gap constraint. In this paper, we propose the global constraint GAP-SEQ enabling to handle SPM with or without gap constraint. GAP-SEQ relies on the principle of right pattern extensions. Experiments show that our approach clearly outperforms both CP approaches and the state-of-the-art cSpade method on large datasets.

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Mining Relevant Sequence Patterns with CP-Based Framework

Cited by 10 publications

References 21 publications

PREFIX-PROJECTION Global Constraint for Sequential Pattern Mining

PREFIX-PROJECTION Global Constraint for Sequential Pattern Mining

Flexible constrained sampling with guarantees for pattern mining

A Global Constraint for Mining Sequential Patterns with GAP Constraint

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