Learning What Matters – Sampling Interesting Patterns

Dzyuba, Vladimir; Leeuwen, Matthijs van

doi:10.1007/978-3-319-57454-7_42

Cited by 13 publications

(33 citation statements)

References 23 publications

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“…These properties are particularly important in interactive mining systems, which aim at returning patterns that are subjectively interesting to the current user. Boley et al [35] used two-step samplers in such a system, while Dzyuba and van Leeuwen [36] proposed to learn low-tilt subjective quality measures specifically for sampling with Flexics.…”

Section: Discussionmentioning

confidence: 99%

Flexible constrained sampling with guarantees for pattern mining

Dzyuba

Leeuwen

Raedt

2017

Data Min Knowl Disc

Self Cite

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Pattern sampling has been proposed as a potential solution to the infamous pattern explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns are sampled proportional to a given quality measure. Several sampling algorithms have been proposed, but each of them has its limitations when it comes to 1) flexibility in terms of quality measures and constraints that can be used, and/or 2) guarantees with respect to sampling accuracy. We therefore present Flexics, the first flexible pattern sampler that supports a broad class of quality measures and constraints, while providing strong guarantees regarding sampling accuracy. To achieve this, we leverage the perspective on pattern mining as a constraint satisfaction problem and build upon the latest advances in sampling solutions in SAT as well as existing pattern mining algorithms. Furthermore, the proposed algorithm is applicable to a variety of pattern languages, which allows us to introduce and tackle the novel task of sampling sets of patterns. We introduce and empirically evaluate two variants of Flexics: 1) a generic variant that addresses the well-known itemset sampling task and the novel pattern set sampling task as well as a wide range of expressive constraints within these tasks, and 2) a specialized variant that exploits existing frequent itemset techniques to achieve substantial speed-ups. Experiments show that Flexics is both accurate and efficient, making it a useful tool for pattern-based data exploration.Comment: Accepted for publication in Data Mining & Knowledge Discovery journal (ECML/PKDD 2017 journal track

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Section: Discussionmentioning

confidence: 99%

Flexible constrained sampling with guarantees for pattern mining

Dzyuba

Leeuwen

Raedt

2017

Data Min Knowl Disc

Self Cite

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“…Our work is based on LetSIP [6], which in turn uses Flexics [8]. In this and the next section, we will therefore sketch the pattern sampling and feedback + preference learning steps of LetSIP (see Algorithm 3) before describing our own proposals in Sections 3 and 4.…”

Section: Letsip: Weighted Constrained Samplingmentioning

confidence: 99%

“…Existing approaches [5][6][7] have a short-coming, however: to enable preference learning, they represent patterns by independent descriptors, such as included items or covered transactions, and expect the learned function, usually a regression or multiplicative weight model, to handle relations. Furthermore, to get quality feedback, it is important to present diverse patterns to the user, an aspect that is at best indirectly handled by existing work.…”

Section: Introductionmentioning

confidence: 99%

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Exploiting complex pattern features for interactive pattern mining

Hien¹,

Loudni²,

Aribi³

et al. 2022

Preprint

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Recent years have seen a shift from a pattern mining process that has users define constraints before-hand, and sift through the results afterwards, to an interactive one. This new framework depends on exploiting user feedback to learn a quality function for patterns. Existing approaches have a weakness in that they use static pre-defined low-level features, and attempt to learn independent weights representing their importance to the user. As an alternative, we propose to work with more complex features that are derived directly from the pattern ranking imposed by the user. Learned weights are then aggregated onto lower-level features and help to drive the quality function in the right direction. We explore the effect of different parameter choices experimentally and find that using higher-complexity features leads to the selection of patterns that are better aligned with a hidden quality function while not adding significantly to the run times of the method. Getting good user feedback requires to quickly present diverse patterns, something that we achieve but pushing an existing diversity constraint into the sampling component of the

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“…Pattern sampling consists in randomly drawing a collection of patterns with a probability proportional to their interest. This technique has a low computational cost, but it is also useful in many tasks such as classification [3], outlier detection [8] or interactive data mining [7]. In this paper, we show how to sample patterns from a distributed database that can be partitioned both horizontally and vertically, without using the computing capacity of the different fragments.…”

Section: Introductionmentioning

confidence: 99%

Pattern Sampling in Distributed Databases

Diop

Giacometti

et al. 2020

Advances in Databases and Information Systems

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Many applications rely on distributed databases. However, only few discovery methods exist to extract patterns without centralizing the data. In fact, this centralization is often less expensive than the communication of extracted patterns from the different nodes. To circumvent this difficulty, this paper revisits the problem of pattern mining in distributed databases by benefiting from pattern sampling. Specifically, we propose the algorithm DDSampling that randomly draws a pattern from a distributed database with a probability proportional to its interest. We demonstrate the soundness of DDSampling and analyze its time complexity. Finally, experiments on benchmark datasets highlight its low communication cost and its robustness. We also illustrate its interest on real-world data from the Semantic Web for detecting outlier entities in DBpedia and Wikidata.

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Learning What Matters – Sampling Interesting Patterns

Cited by 13 publications

References 23 publications

Flexible constrained sampling with guarantees for pattern mining

Flexible constrained sampling with guarantees for pattern mining

Exploiting complex pattern features for interactive pattern mining

Pattern Sampling in Distributed Databases

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