An Efficient Framework for Mining Flexible Constraints

Soulet, Arnaud; Crémilleux, Bruno

doi:10.1007/11430919_76

Cited by 20 publications

(21 citation statements)

References 14 publications

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“…Constraint pattern mining aims at classifying existing constraints into constraint classes such as anti-monotone constraints (Mannila and Toivonen, 1997), monotone constraints (Mannila and Toivonen, 1997), succinct constraints (Ng et al, 1998), convertible constraints Pei et al, 2001), loose anti-monotone constraints (Bonchi and Lucchese, 2007) and the recent area constraint (Soulet and Crémilleux, 2005).…”

Section: Constraint Based Itemset Miningmentioning

confidence: 99%

Para Miner: a generic pattern mining algorithm for multi-core architectures

Négrevergne

Termier

Rousset

et al. 2013

Data Min Knowl Disc

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In this paper, we present ParaMiner which is a generic and parallel algorithm for closed pattern mining.ParaMiner is built on the principles of pattern enumeration in strongly accessible set systems. Its efficiency is due to a novel dataset reduction technique (that we call EL-reduction), combined with novel technique for performing dataset reduction in a parallel execution on a multi-core architecture.We illustrate ParaMiner's genericity by using this algorithm to solve three different pattern mining problems: the frequent itemset mining problem, the mining frequent connected relational graphs problem and the mining gradual itemsets problem.In this paper, we prove the soundness and the completeness of ParaMiner. Furthermore, our experiments show that despite being a generic algorithm, ParaMiner can compete with specialized state of the art algorithms designed for the pattern mining problems mentioned above. Besides, for the particular problem of gradual itemset mining, ParaMiner outperforms the state of the art algorithm by two orders of magnitude.

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Section: Constraint Based Itemset Miningmentioning

confidence: 99%

Para Miner: a generic pattern mining algorithm for multi-core architectures

Négrevergne

Termier

Rousset

et al. 2013

Data Min Knowl Disc

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“…This idea was generalized in [19,25,6]. In [19] it was shown how to compute witnesses for the more difficult "variance" constraint, a problem that remained opened for several years in the data mining community.…”

Section: Specialized Approachesmentioning

confidence: 99%

“…In [19] it was shown how to compute witnesses for the more difficult "variance" constraint, a problem that remained opened for several years in the data mining community. Soulet et al [25] extend the idea to deal with more difficult constraints such as the area constraint, which take into account both support sets and itemsets. For example, if we want to compute all the patterns (X, Y ) satisfying C itemset with an area greater than 3 (C area where α = 3), knowing that ⊥ I ⊆ Y ⊆ I and hence support( I ) ⊆ X ⊆ support(⊥ I ), then we can bound the area of (X, Y ) by |support(…”

Section: Specialized Approachesmentioning

confidence: 99%

“…Most of the recent set pattern mining algorithms can exploit constraints that are not limited to the simple cases of monotonic and/or antimonotonic ones as described in, for instance, [9]. New concepts have emerged like "flexible constraints" [25], "witnesses" [19] or "soft constraints" [4]. Also, the specific problems of noisy data sets has inspired a constraint-based mining view on fault-tolerance (see, e.g., [24,28,3]).…”

Section: Introductionmentioning

confidence: 99%

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Generalizing Itemset Mining in a Constraint Programming Setting

Besson

Boulicaut

Guns

et al. 2010

Inductive Databases and Constraint-Based Data Mining

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In recent years, a large number of algorithms have been proposed for finding set patterns in boolean data. This includes popular mining tasks based on, for instance, frequent (closed) itemsets. In this chapter, we develop a common framework in which these algorithms can be studied thanks to the principles of constraint programming. We show how such principles can be applied both in specialized and general solvers.

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“…Constraints allow user to focus on the most promising knowledge by reducing the number of extracted patterns to those of potential interest. There are now generic approaches to discover patterns and sequential patterns under constraints (e.g., De Raedt et al, 2002;Soulet and Crémilleux, 2005;Pei et al, 2002;Garofalakis et al, 1999;Leleu et al, 2003). Note that constraint-based pattern mining challenges two major problems in pattern mining: effectiveness and efficiency.…”

Section: Preliminary Definitionsmentioning

confidence: 99%

Combining sequence and itemset mining to discover named entities in biomedical texts: a new type of pattern

Plantevit

Charnois

Kléma

et al. 2009

IJDMMM

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Biomedical named entity recognition (NER) is a challenging problem. In this paper, we show that mining techniques, such as sequential pattern mining and sequential rule mining, can be useful to tackle this problem but present some limitations. We demonstrate and analyse these limitations and introduce a new kind of pattern called LSR pattern that offers an excellent trade-off between the high precision of sequential rules and the high recall of sequential patterns. We formalise the LSR pattern mining problem first. Then we show how LSR patterns enable us to successfully tackle biomedical NER problems. We report experiments carried out on real datasets that underline the relevance of our proposition.

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An Efficient Framework for Mining Flexible Constraints

Cited by 20 publications

References 14 publications

Para Miner: a generic pattern mining algorithm for multi-core architectures

Para Miner: a generic pattern mining algorithm for multi-core architectures

Generalizing Itemset Mining in a Constraint Programming Setting

Combining sequence and itemset mining to discover named entities in biomedical texts: a new type of pattern

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