A Global Constraint for Mining Sequential Patterns with GAP Constraint

Kemmar, Amina; Loudni, Samir; Lebbah, Yahia; Boizumault, Patrice; Charnois, Thierry

doi:10.1007/978-3-319-33954-2_15

Cited by 11 publications

(22 citation statements)

References 18 publications

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“…Recently, constraint programming (CP) has emerged as a successful tool for CSPM (Negrevergne and Guns 2015;Kemmar et al 2016;Aoga, Guns, and Schaus 2017;Guns et al 2017). CP search techniques, albeit general, can potentially be more efficient when compared to specialized CSPM algorithms.…”

Section: Related Workmentioning

confidence: 99%

Constraint-Based Sequential Pattern Mining with Decision Diagrams

Hosseininasab

Hoeve

Ciré

2019

AAAI

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Constrained sequential pattern mining aims at identifying frequent patterns on a sequential database of items while observing constraints defined over the item attributes. We introduce novel techniques for constraint-based sequential pattern mining that rely on a multi-valued decision diagram representation of the database. Specifically, our representation can accommodate multiple item attributes and various constraint types, including a number of non-monotone constraints. To evaluate the applicability of our approach, we develop an MDD-based prefix-projection algorithm and compare its performance against a typical generate-and-check variant, as well as a state-of-the-art constraint-based sequential pattern mining algorithm. Results show that our approach is competitive with or superior to these other methods in terms of scalability and efficiency.

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Section: Related Workmentioning

confidence: 99%

Constraint-Based Sequential Pattern Mining with Decision Diagrams

Hosseininasab

Hoeve

Ciré

2019

AAAI

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“…Kemmar et al [7] propose to gather all these constraints into a unique global constraint to reduce the overhead of the multiple constraints. They further showed how the constraint can be modified to take a maximal gap constraint into account [5].…”

Section: Cp-based Approaches For Spmmentioning

confidence: 99%

“…Although being the state-of-art approach for solving SPM with CP, the filtering algorithm of Kemmar et al [5] presents room for improvement. We identify four weaknesses and propose solutions to them.…”

Section: Improving Propagationmentioning

confidence: 99%

“…In recent years, constraint programming (CP) has been proposed as a general framework for pattern mining [2,3,4,5]. The main benefit of CP-based approaches over dedicated algorithms is that it is modular.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, by using a global constraint that computes the pseudo-projection of the sequences in the database similar to Pre-fixSpan [6], this overhead can be reduced. Kemmar et al [5,7] have further extended this work by introducing one constraint (module) for both the pseudoprojection and the frequency pruning. While reasonable performance is obtained, especially for mining under regular expressions, the method does not achieve the same scalability as some of the best specialized systems such as Zaki's cSpade [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Algorithm for Mining Frequent Sequence with Constraint Programming

Aoga

Guns

Schaus

2016

Machine Learning and Knowledge Discovery in Databases

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The main advantage of Constraint Programming (CP) approaches for sequential pattern mining (SPM) is their modularity, which includes the ability to add new constraints (regular expressions, length restrictions, etc). The current best CP approach for SPM uses a global constraint (module) that computes the projected database and enforces the minimum frequency; it does this with a filtering algorithm similar to the PrefixSpan method. However, the resulting system is not as scalable as some of the most advanced mining systems like Zaki's cSPADE. We show how, using techniques from both data mining and CP, one can use a generic constraint solver and yet outperform existing specialized systems. This is mainly due to two improvements in the module that computes the projected frequencies: first, computing the projected database can be sped up by pre-computing the positions at which an symbol can become unsupported by a sequence, thereby avoiding to scan the full sequence each time; and second by taking inspiration from the trailing used in CP solvers to devise a backtracking-aware data structure that allows fast incremental storing and restoring of the projected database. Detailed experiments show how this approach outperforms existing CP as well as specialized systems for SPM, and that the gain in efficiency translates directly into increased efficiency for other settings such as mining with regular expressions.

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Behavioral Constraint Template-Based Sequence Classification

Smedt

Deeva

Weerdt

2017

Machine Learning and Knowledge Discovery in Databases

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In this paper we present the interesting Behavioral Constraint Miner (iBCM), a new approach towards classifying sequences. The prevalence of sequential data, i.e., a collection of ordered items such as text, website navigation patterns, traffic management, and so on, has incited a surge in research interest towards sequence classification. Existing approaches mainly focus on retrieving sequences of itemsets and checking their presence in labeled data streams to obtain a classifier. The proposed iBCM approach, rather than focusing on plain sequences, is template-based and draws its inspiration from behavioral patterns used for software verification. These patterns have a broad range of characteristics and go beyond the typical sequence mining representation, allowing for a more precise and concise way of capturing sequential information in a database. Furthermore, it is possible to also mine for negative information, i.e., sequences that do not occur. The technique is benchmarked against other state-of-the-art approaches and exhibits a strong potential towards sequence classification.

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A Global Constraint for Mining Sequential Patterns with GAP Constraint

Cited by 11 publications

References 18 publications

Constraint-Based Sequential Pattern Mining with Decision Diagrams

Constraint-Based Sequential Pattern Mining with Decision Diagrams

An Efficient Algorithm for Mining Frequent Sequence with Constraint Programming

Behavioral Constraint Template-Based Sequence Classification

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