Mining Dominant Patterns in the Sky

Soulet, Arnaud; Raïssi, Chedy; Plantevit, Marc; Crémilleux, Bruno

doi:10.1109/icdm.2011.100

Cited by 48 publications

(57 citation statements)

References 23 publications

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“…For that matter, we can simply keep in memory patterns that are not dominated by others while enumerating them. In other words, we incrementally build the Pareto front given a set of user preferences [so called skypatterns (Soulet et al 2011)].…”

Section: Discussionmentioning

confidence: 99%

Exceptional contextual subgraph mining

et al. 2017

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Many relational data result from the aggregation of several individual behaviors described by some characteristics. For instance, a bike-sharing system may be modeled as a graph where vertices stand for bike-share stations and connections represent bike trips made by users from one station to another. Stations and trips are described by additional information such as the description of the geographical environment of the stations (business vs. residential area, closeness to POI, elevation, urbanization density, etc.), or properties of the bike trips (timestamp, user profile, weather, events and other special conditions about the trip). Identifying highly connected components (such as communities or quasi-cliques) in this graph provides interesting insights into global usages but does not capture mobility profiles that characterize a subpopulation. To tackle this problem we propose an approach rooted in exceptional model mining to find exceptional contextual subgraphs, i.e., subgraphs generated from a context or a description of the individual behaviors that is exceptional (behaves in a different way) compared to the whole augmented graph. The dependency between a context and an edge is assessed by a χ 2 test and the weighted relative accuracy measure is used to only retain contexts that strongly characterize connected subgraphs. We present an original algorithm that uses sophisticated pruning techniques to restrict the search space of vertices, context refinements, and edges to be considered. An experimental evaluation on synthetic data and two real-life datasets demonstrates the effectiveness of the proposed pruning mechanisms, as well as the relevance of the discovered patterns.

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Section: Discussionmentioning

confidence: 99%

Exceptional contextual subgraph mining

et al. 2017

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“…Skypatterns have been recently introduced by [19]. Such patterns enable to express a user-preference point of view according to a dominance relation.…”

Section: Skypatternsmentioning

confidence: 99%

“…[19] have proposed an approach taking benefit of theoretical relationships between pattern condensed representations and skypatterns and making the process feasible when the pattern condensed representation can be extracted. Nevertheless, this method can only use a crisp dominance relation.…”

Section: Related Workmentioning

confidence: 99%

“…[17] proposes a technique to extract skyline graphs that maximize two measures (the number of vertices and the edge connectivity). The notion of skyline queries has been recently integrated into the constraint-based pattern discovery paradigm to mine skyline patterns (henceforth called skypatterns) [19]. As an example, a user may prefer a pattern with a high frequency, large length and a high confidence.…”

Section: Introductionmentioning

confidence: 99%

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Mining (Soft-) Skypatterns Using Dynamic CSP

Ugarte¹,

Boizumault²,

Loudni³

et al. 2014

Lecture Notes in Computer Science

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Abstract. Within the pattern mining area, skypatterns enable to express a userpreference point of view according to a dominance relation. In this paper, we deal with the introduction of softness in the skypattern mining problem. First, we show how softness can provide convenient patterns that would be missed otherwise. Then, thanks to Constraint Programming, we propose a generic and efficient method to mine skypatterns as well as soft ones. Finally, we show the relevance and the effectiveness of our approach through a case study in chemoinformatics.

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“…Skyrules Algorithm [4] proposes a statistical dominance-based algorithm which distinguishes dominant from non-dominant rules; the algorithm keeps the former and discards the latter with complete reliance on skyline operator. This techniques is only an extended exploitation of the technique proposed in [19] which is based on the notion of dominance to generate dominant patterns and reject dominated ones with regard to skyline operators [20]. In [1], authors are interested in modeling and automating the mining process of relevant ARs.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards an enhanced user’s preferences integration into ranking process using dominance approach

Mohammed

Balouki

Gadi

2017

Vietnam J Comput Sci

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User preference is very important in orienting data miner, and this is the reason why these user preferences are integrated in the mining process, where they are coupled with Association Rules Mining "ARM" Algorithms to select only Association Rules "ARs" that satisfy the user's wishes and expectations. Within this framework, several approaches were proposed to overcome some problems which persist with the traditional ARM algorithms mainly dimensionality phenomenon engendered by thresholding and the subjective choice of measures. "MDP REF Algorithm" is one of these approaches; it prunes, filters to select the relevant ARs, while "Rank-Sort-MDP REF

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Mining Dominant Patterns in the Sky

Cited by 48 publications

References 23 publications

Exceptional contextual subgraph mining

Exceptional contextual subgraph mining

Mining (Soft-) Skypatterns Using Dynamic CSP

Towards an enhanced user’s preferences integration into ranking process using dominance approach

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