Prefix-projection global constraint and top-k approach for sequential pattern mining View Full Text


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Article Info

DATE

2017-04

AUTHORS

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

ABSTRACT

Sequential pattern mining (SPM) is an important data mining problem with broad applications. SPM is a hard problem due to the huge number of intermediate subsequences to be considered. State of the art approaches for SPM (e.g., PrefixSpan Pei et al. 2001) are largely based on the pattern-growth approach, where for each frequent prefix subsequence, only its related suffix subsequences need to be considered, and the database is recursively projected into smaller ones. Many authors have promoted the use of constraints to focus on the most promising patterns according to the interests of the end user. The top-k SPM problem is also used to cope with the difficulty of thresholding and to control the number of solutions. State of the art methods developed for SPM and top-k SPM, though efficient, are locked into a rather rigid search strategy, and suffer from the lack of declarativity and flexibility. Indeed, adding new constraints usually amounts to changing the data-structures used in the core of the algorithm, and combining these new constraints often require new developments. Recent works (e.g. Kemmar et al. 2014; Négrevergne and Guns 2015) have investigated the use of Constraint Programming (CP) for SPM. However, despite their nice declarative aspects, all these modelings have scaling problems, due to the huge size of their constraint networks. To address this issue, we propose the Prefix-Projection global constraint, which encapsulates both the subsequence relation as well as the frequency constraint. Its filtering algorithm relies on the principle of projected databases which allows to keep in the variables domain, only values leading to a frequent pattern in the database. Prefix-Projection filtering algorithm enforces domain consistency on the variable succeeding the current frequent prefix in polynomial time. This global constraint also allows for a straightforward implementation of additional constraints such as size, item membership, regular expressions and any combination of them. Experimental results show that our approach clearly outperforms existing CP approaches and competes well with the state-of-the-art methods on large datasets for mining frequent sequential patterns, sequential patterns under various constraints, and top-k sequential patterns. Unlike existing CP methods, our approach achieves a better scalability. More... »

PAGES

265-306

References to SciGraph publications

  • 1996. Mining sequential patterns: Generalizations and performance improvements in ADVANCES IN DATABASE TECHNOLOGY — EDBT '96
  • 2000. Mining Access Patterns Efficiently from Web Logs in KNOWLEDGE DISCOVERY AND DATA MINING. CURRENT ISSUES AND NEW APPLICATIONS
  • 2014. Mining (Soft-) Skypatterns Using Dynamic CSP in INTEGRATION OF AI AND OR TECHNIQUES IN CONSTRAINT PROGRAMMING
  • 2014-07. Mining top-k frequent patterns with combination reducing techniques in APPLIED INTELLIGENCE
  • 2001-01. SPADE: An Efficient Algorithm for Mining Frequent Sequences in MACHINE LEARNING
  • 2015. PREFIX-PROJECTION Global Constraint for Sequential Pattern Mining in PRINCIPLES AND PRACTICE OF CONSTRAINT PROGRAMMING
  • 2015. Constraint-Based Sequence Mining Using Constraint Programming in INTEGRATION OF AI AND OR TECHNIQUES IN CONSTRAINT PROGRAMMING
  • 2004. A Regular Language Membership Constraint for Finite Sequences of Variables in PRINCIPLES AND PRACTICE OF CONSTRAINT PROGRAMMING – CP 2004
  • 2013. TKS: Efficient Mining of Top-K Sequential Patterns in ADVANCED DATA MINING AND APPLICATIONS
  • 2016. A Global Constraint for Mining Sequential Patterns with GAP Constraint in INTEGRATION OF AI AND OR TECHNIQUES IN CONSTRAINT PROGRAMMING
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    37 schema:description Sequential pattern mining (SPM) is an important data mining problem with broad applications. SPM is a hard problem due to the huge number of intermediate subsequences to be considered. State of the art approaches for SPM (e.g., PrefixSpan Pei et al. 2001) are largely based on the pattern-growth approach, where for each frequent prefix subsequence, only its related suffix subsequences need to be considered, and the database is recursively projected into smaller ones. Many authors have promoted the use of constraints to focus on the most promising patterns according to the interests of the end user. The top-k SPM problem is also used to cope with the difficulty of thresholding and to control the number of solutions. State of the art methods developed for SPM and top-k SPM, though efficient, are locked into a rather rigid search strategy, and suffer from the lack of declarativity and flexibility. Indeed, adding new constraints usually amounts to changing the data-structures used in the core of the algorithm, and combining these new constraints often require new developments. Recent works (e.g. Kemmar et al. 2014; Négrevergne and Guns 2015) have investigated the use of Constraint Programming (CP) for SPM. However, despite their nice declarative aspects, all these modelings have scaling problems, due to the huge size of their constraint networks. To address this issue, we propose the Prefix-Projection global constraint, which encapsulates both the subsequence relation as well as the frequency constraint. Its filtering algorithm relies on the principle of projected databases which allows to keep in the variables domain, only values leading to a frequent pattern in the database. Prefix-Projection filtering algorithm enforces domain consistency on the variable succeeding the current frequent prefix in polynomial time. This global constraint also allows for a straightforward implementation of additional constraints such as size, item membership, regular expressions and any combination of them. Experimental results show that our approach clearly outperforms existing CP approaches and competes well with the state-of-the-art methods on large datasets for mining frequent sequential patterns, sequential patterns under various constraints, and top-k sequential patterns. Unlike existing CP methods, our approach achieves a better scalability.
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