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

Négrevergne, Benjamin; Termier, Alexandre; Rousset, Marie-Christine; Méhaut, Jean‐François

doi:10.1007/s10618-013-0313-2

Cited by 50 publications

(26 citation statements)

References 29 publications

(54 reference statements)

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“…in G degrees in St at least k and degrees in Sa at least 1 Zaversnik 2011), and has been performed on very large networks. Regarding the closed pattern enumeration, our algorithm is based on an efficient top-down general algorithm (Boley et al 2010) and the implementation uses data reduction techniques borrowed from (Negrevergne et al 2013). However the scalability, as mentioned above, depends on the number of bi-patterns to generate.…”

Section: Scalabilitymentioning

confidence: 99%

Bi-pattern mining of attributed networks

et al. 2019

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Applying closed pattern mining to attributed two-mode networks requires two conditions. First, as in two-mode networks there are two kinds of vertices, each described with a proper attribute set, we have to consider patterns made of two components that we call bi-patterns. The occurrences of a bi-pattern forms an extension made of a pair of vertex subsets. Second, Formal Concept Analysis and Closed Pattern Mining were recently applied to networks by reducing the extensions of pattern to their cores, according to some core definition. We need to consider appropriate core definitions for two-mode networks and define accordingly closed bi-patterns. We describe in this article a general framework to define closed bi-pattern mining. We also show that this methodology applies as well to cores of directed and undirected networks in which each vertex subset is associated with a specific role. We illustrate the methodology first on a two-mode network of epistemological data, then on a directed advice network of lawyers and finally on an undirected bibliographical network.

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Section: Scalabilitymentioning

confidence: 99%

Bi-pattern mining of attributed networks

et al. 2019

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“…Declarative and generic approaches for pattern mining have been studied by many researchers under different angles (databases, constraint programming or theoretical foundations), see for example [33,40,7,24,15,31,44,47,46,48]. A crucial question underlying all these approaches is about the languages to first specify the data of interest and second, to express patterns of interest.…”

Section: Related Workmentioning

confidence: 99%

RQL: A Query Language for Rule Discovery in Databases

Chardin

Coquery

Pailloux

et al. 2017

Theoretical Computer Science

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“…This method guarantees a best effort locality. More details can be found in the description of Melinda given in (Negrevergne, 2011).…”

Section: Parallelizationmentioning

confidence: 99%

PGLCM: efficient parallel mining of closed frequent gradual itemsets

et al. 2014

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International audienceNumerical data (e.g., DNA micro-array data, sensor data) pose a challenging problem to existing frequent pattern mining methods which hardly handle them. In this framework, gradual patterns have been recently proposed to extract covariations of attributes, such as: “When X increases, Y decreases”. There exist some algorithms for mining frequent gradual patterns, but they cannot scale to real-world databases. We present in this paper GLCM, the first algorithm for mining closed frequent gradual patterns, which proposes strong complexity guarantees: the mining time is linear with the number of closed frequent gradual itemsets. Our experimental study shows that GLCM is two orders of magnitude faster than the state of the art, with a constant low memory usage. We also present PGLCM, a parallelization of GLCM capable of exploiting multicore processors, with good scale-up properties on complex datasets. These algorithms are the first algorithms capable of mining large real world datasets to discover gradual patterns

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

Cited by 50 publications

References 29 publications

Bi-pattern mining of attributed networks

Bi-pattern mining of attributed networks

RQL: A Query Language for Rule Discovery in Databases

PGLCM: efficient parallel mining of closed frequent gradual itemsets

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