CoverSize: A Global Constraint for Frequency-Based Itemset Mining

Schaus, Pierre; Aoga, John O. R.; Guns, Tias

doi:10.1007/978-3-319-66158-2_34

Cited by 21 publications

(23 citation statements)

References 31 publications

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“…In these two venues, we find papers using CP for data analysis. For instance, CP can be used for solving pattern mining, sequence mining, clustering, and other data mining problems [Ugarte et al, 2017;Dao et al, 2017;Schaus et al, 2017]. (The first CP model for pattern mining had been proposed by Guns et al [2011].)…”

Section: Conclusion and New Trendsmentioning

confidence: 99%

Constraint Reasoning

Bessière

2020

A Guided Tour of Artificial Intelligence Research

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In this chapter, I briefly present constraint reasoning. Constraint reasoning has been a subfield of artificial intelligence (AI) that is nowadays more well-known as constraint programming (CP). The change of name occurred more or less when CP has started to be widely used for solving combinatorial problems in industrial applications. This also corresponds to the moment where CP was enriched by the contributions from logic programming for the aspects related to languages and from operation research for the propagation of complex constraints. Considering the topic of this book, I will stay on a AI-oriented presentation of CP. * This chapter belongs to the book [Marquis et al., 2020]. It is essentially the English version of a chapter written in 2010 for the French book [Marquis et al., 2014]. A few references to more recent contributions have been added but the global structure has not been modified.

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Section: Conclusion and New Trendsmentioning

confidence: 99%

Constraint Reasoning

Bessière

2020

A Guided Tour of Artificial Intelligence Research

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“…We note also the multitude of recent work in integrating CP and datamining/machine learning. On the one hand, the CP-based approaches offer a trade-off between generality and efficiency for some classic datamining problems, such as clustering (Dao, Duong, and Vrain 2017) and frequent pattern mining (Guns, Nijssen, and Raedt 2011;Lazaar et al 2016;Schaus, Aoga, and Guns 2017;Guns et al 2017). On the other hand, a number of studies have shown the benefits of applying machine learning in constraint solving (Epstein and Petrovic 2007;Xu, Stern, and Samulowitz 2009;Loth et al 2013;Hurley et al 2014;Chu and Stuckey 2015;Balafrej, Bessiere, and Paparrizou 2015;Bachiri et al 2015;Xia and Yap 2018;Cappart et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Finding Good Subtrees for Constraint Optimization Problems Using Frequent Pattern Mining

Lee

et al. 2020

AAAI

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Making good decisions at the top of a search tree is important for finding good solutions early in constraint optimization. In this paper, we propose a method employing frequent pattern mining (FPM), a classic datamining technique, to find good subtrees for solving constraint optimization problems. We demonstrate that applying FPM in a small number of random high-quality feasible solutions enables us to identify subtrees containing optimal solutions in more than 55% of problem instances for four real world benchmark problems. The method works as a plugin that can be combined with any search strategy for branch-and-bound search. Exploring the identified subtrees first, the method brings substantial improvements for four efficient search strategies in both total runtime and runtime of finding optimal solutions.

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“…The sparse bit-set is represented as an array of words (typically, words of 64 bits) and is sparse: operations performed on it by the propagator only consider words that have at least one bit set (that is, non-zero or non-empty words), where the emptiness information is tracked by an index structure. Compact-table and its extensions have already shown great potential [5,15,14] and sparse bit-sets have also been successfully used for itemset mining constraints [12].…”

Section: Introductionmentioning

confidence: 99%

Making Compact-Table Compact

Ingmar

Schulte

2018

Lecture Notes in Computer Science

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The compact-table propagator for table constraints appears to be a strong candidate for inclusion into any constraint solver due to its efficiency and simplicity. However, successful integration into a constraint solver based on copying rather than trailing is not obvious: while the underlying bit-set data structure is sparse for efficiency it is not compact for memory, which is essential for a copying solver. The paper introduces techniques to make compact-table an excellent fit for a copying solver. The key is to make sparse bit-sets dynamically compact (only their essential parts occupy memory and their implementation is dynamically adapted during search) and tables shared (their read-only parts are shared among copies). Dynamically compact bit-sets reduce peak memory by 7.2% and runtime by 13.6% on average and by up to 66.3% and 33.2%. Shared tables even further reduce runtime and memory usage. The reduction in runtime exceeds the reduction in memory and a cache analysis indicates that our techniques might also be beneficial for trailing solvers. The proposed implementation has replaced Gecode's original implementations as it runs on average almost an order of magnitude faster while using half the memory.

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CoverSize: A Global Constraint for Frequency-Based Itemset Mining

Cited by 21 publications

References 31 publications

Constraint Reasoning

Constraint Reasoning

Finding Good Subtrees for Constraint Optimization Problems Using Frequent Pattern Mining

Making Compact-Table Compact

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