Exceptionally monotone models—the rank correlation model class for Exceptional Model Mining

Downar, Lennart; Duivesteijn, Wouter

doi:10.1007/s10115-016-0979-z

Cited by 8 publications

(14 citation statements)

References 45 publications

(18 reference statements)

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“…Furthermore, the single target can be discrete or numeric (Lemmerich et al, 2016). Exceptional Model Mining (EMM) (Leman et al, 2008), while sharing exactly the same exploration space (i.e., the description space), extends SD by offering the possibility to handle complex targets, e.g., several discrete attributes (van Leeuwen and Knobbe, 2012;Duivesteijn et al, 2010Duivesteijn et al, , 2016, graphs (Kaytoue et al, 2017;Bendimerad et al, 2016Bendimerad et al, , 2017, two numeric targets (Downar and Duivesteijn, 2017) and preferences (de Sá et al, 2016(de Sá et al, , 2018. Our method is rooted in the SD/EMM framework.…”

Section: Related Workmentioning

confidence: 99%

Identifying exceptional (dis)agreement between groups

Belfodil

Cazalens

Lamarre

et al. 2019

Data Min Knowl Disc

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Under the term behavioral data, we consider any type of data featuring individuals performing observable actions on entities. For instance, voting data depict parliamentarians who express their votes w.r.t. legislative procedures. In this work, we address the problem of discovering exceptional (dis)agreement patterns in such data, i.e., groups of individuals that exhibit an unexpected (dis)agreement under specific contexts compared to what is observed in overall terms. To tackle this problem, we design a generic approach, rooted in the Subgroup Discovery/Exceptional Model Mining framework, which enables the discovery of such patterns in two different ways. A branch-and-bound algorithm ensures an efficient exhaustive search of the underlying search space by leveraging closure operators and optimistic estimates on the interestingness measures. A second algorithm abandons the completeness by using a sampling paradigm which provides an alternative when an exhaustive search approach becomes unfeasible. To illustrate the usefulness of discovering exceptional (dis)agreement patterns, we report a comprehensive experimental study on four real-world datasets relevant to three different application domains: political analysis, rating data analysis and healthcare surveillance.

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Section: Related Workmentioning

confidence: 99%

Identifying exceptional (dis)agreement between groups

Belfodil

Cazalens

Lamarre

et al. 2019

Data Min Knowl Disc

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show abstract

“…Furthermore, the single target can be discrete or numeric [22]. Exceptional Model Mining (EMM) [21], while sharing the same exploration space (i.e., the description space), extends SD by offering the possibility to handle complex targets, e.g., several discrete attributes [20,9], two numeric targets [8] and preferences [24].…”

Section: Related Workmentioning

confidence: 99%

Exceptional Attributed Subgraph Mining to Understand the Olfactory Percept

et al. 2018

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Human olfactory perception is a complex phenomenon whose neural mechanisms are still largely unknown and novel methods are needed to better understand it. Methodological issues that prevent such understanding are: (1) to be comparable, individual cerebral images have to be transformed in order to fit a template brain, leading to a spatial imprecision that has to be taken into account in the analysis; (2) we have to deal with inter-individual variability of the hemodynamic signal from fMRI images which render comparisons of individual raw data difficult. The aim of the present paper was to overcome these issues. To this end, we developed a methodology based on discovering exceptional attributed subgraphs which enabled extracting invariants from fMRI data of a sample of individuals breathing different odorant molecules.Four attributed graph models were proposed that differ in how they report the hemodynamic activity measured in each voxel by associating varied attributes to the vertices of the graph. An extensive empirical study is presented that compares the ability of each modeling to uncover some brain areas that are of interest for the neuroscientists.

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“…The choice of a pattern 128.24 ≤ a ≤ 152. 16 21 ≤ b ≤ 29 9 ≤ c ≤ 12 Figure 2: The upper part of the search space for Table 1. quality measure, denoted ϕ in what follows, is generally application dependant as explained by [22].…”

Section: Pattern Set Discoverymentioning

confidence: 99%

“…Firstly, we gathered benchmark datasets used in the recent literature of SD and EMM, that is, from [53,16,51,52,18]. Table 3 lists them, mainly taken from the UCI repository, and we provide some of their properties.…”

Section: Datamentioning

confidence: 99%

“…direct-freq-roll-out top-10-mean-reward (7) direct-freq-roll-out random-reward (8) large-freq-roll-out (jumpLength = 10) max-reward (9) large-freq-roll-out (jumpLength = 10) mean-reward (10) large-freq-roll-out (jumpLength = 10) top-2-mean-reward (11) large-freq-roll-out (jumpLength = 10) top-5-mean-reward (12) large-freq-roll-out (jumpLength = 10) top-10-mean-reward (13) large-freq-roll-out (jumpLength = 10) random-reward (14) large-freq-roll-out (jumpLength = 20) max-reward (15) large-freq-roll-out (jumpLength = 20) mean-reward (16) large-freq-roll-out (jumpLength = 20) top-2-mean-reward (17) large-freq-roll-out (jumpLength = 20) top-5-mean-reward (18) large-freq-roll-out (jumpLength = 20) top-10-mean-reward (19) large-freq-roll-out (jumpLength = 20) random-reward (20) large-freq-roll-out (jumpLength = 50) max-reward (21) large-freq-roll-out (jumpLength = 50) mean-reward (22) large-freq-roll-out (jumpLength = 50) top-2-mean-reward (23) large-freq-roll-out (jumpLength = 50) top-5-mean-reward (24) large-freq-roll-out (jumpLength = 50) top-10-mean-reward (25) large-freq-roll-out (jumpLength = 50) random-reward (26) large-freq-roll-out (jumpLength = 100) max-reward (27) large-freq-roll-out (jumpLength = 100) mean-reward (28) large-freq-roll-out (jumpLength = 100) top-2-mean-reward (29) large-freq-roll-out (jumpLength = 100) top-5-mean-reward (30) large-freq-roll-out (jumpLength = 100) top-10-mean-reward (31) large-freq-roll-out (jumpLength = 100) random-reward room dataset. Finally, the description length is not or almost not influenced by the Roll-Out strategies.…”

Section: Strategymentioning

confidence: 99%

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Anytime discovery of a diverse set of patterns with Monte Carlo tree search

Bosc

Boulicaut

Raïssi

et al. 2017

Data Min Knowl Disc

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The discovery of patterns that accurately discriminate one class label from another remains a challenging data mining task. Subgroup discovery (SD) is one of the frameworks that enables to elicit such interesting patterns from labeled data. A question remains fairly open: How to select an accurate heuristic search technique when exhaustive enumeration of the pattern space is infeasible? Existing approaches make use of beam-search, sampling, and genetic algorithms for discovering a pattern set that is nonredundant and of high quality w.r.t. a pattern quality measure. We argue that such approaches produce pattern sets that lack of diversity: Only few patterns of high quality, and different enough, are discovered. Our main contribution is then to formally define pattern mining as a game and to solve it with Monte Carlo tree search (MCTS). It can be seen as an exhaustive search guided by random simulations which can be stopped early (limited budget) by virtue of its best-first search property. We show through a comprehensive set of experiments how MCTS enables the anytime discovery of a diverse pattern set of high quality. It outperforms other approaches when dealing with a large pattern search space and for different quality measures. Thanks to its genericity, our MCTS approach can be used for SD but also for many other pattern mining tasks.

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Exceptionally monotone models—the rank correlation model class for Exceptional Model Mining

Cited by 8 publications

References 45 publications

Identifying exceptional (dis)agreement between groups

Identifying exceptional (dis)agreement between groups

Exceptional Attributed Subgraph Mining to Understand the Olfactory Percept

Anytime discovery of a diverse set of patterns with Monte Carlo tree search

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