Fast exhaustive subgroup discovery with numerical target concepts

Lemmerich, Florian; Atzmueller, Martin; Puppe, Frank

doi:10.1007/s10618-015-0436-8

Cited by 52 publications

(52 citation statements)

References 45 publications

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“…One group of techniques focusses on finding subgroups where the target shows a surprisingly high (or conversely, low) average value (Grosskreutz and Rüping 2009;Atzmüller and Lemmerich 2009;Pieters et al 2010;Lemmerich et al 2015). Typical proposed quality measures use statistical tests to capture the level of deviation within the subgroup, often weighted by the size of the subgroup, for example the mean test or z-score (Pieters et al 2010),…”

Section: Subgroup Discoverymentioning

confidence: 99%

Sports analytics for professional speed skating

Knobbe

Orie

Hofman

et al. 2017

Data Min Knowl Disc

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In elite sports, training schedules are becoming increasingly complex, and a large number of parameters of such schedules need to be tuned to the specific physique of a given athlete. In this paper, we describe how extensive analysis of historical data can help optimise these parameters, and how possible pitfalls of under-and overtraining in the past can be avoided in future schedules. We treat the series of exercises an athlete undergoes as a discrete sequence of attributed events, that can be aggregated in various ways, to capture the many ways in which an athlete can prepare for an important test event. We report on a cooperation with the elite speed skating team LottoNLJumbo, who have recorded detailed training data over the last 15 years. The aim of the project was to analyse this potential source of knowledge, and extract actionable and interpretable patterns that can provide input to future improvements in training. We present two alternative techniques to aggregate sequences of exercises into a combined, long-term training effect, one of which based on a sliding window, and one based on a physiological model of how the body responds to exercise. Next, we use both linear modelling and Subgroup Discovery to extract meaningful models of the data.

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Section: Subgroup Discoverymentioning

confidence: 99%

Sports analytics for professional speed skating

Knobbe

Orie

Hofman

et al. 2017

Data Min Knowl Disc

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“…1 summarizes all of the above ideas. Note that, for the sake of clarity, we omitted here some other common parameters such as a depth-limit and multiple solutions (top-k), which are straightforward to incorporate (see Lemmerich et al, 2016). An efficiently computable refinement operator has to be constructed specifically for the desired description language.…”

Section: Branch-and-bound and Optimistic Estimatorsmentioning

confidence: 99%

“…which has been shown to be a crucial ingredient for the practical applicability of branchand-bound (Grosskreutz et al, 2008;Lemmerich et al, 2016). So far, the most general approach to this problem (first codified in Lemmerich et al (2016); generalized here in Sec. 3.1) is to maintain a sorted list of target values throughout the search process and then to compute Eq.…”

Section: Introductionmentioning

confidence: 99%

Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery

Boley

Goldsmith

Ghiringhelli

et al. 2017

Data Min Knowl Disc

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Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (non-increasing dependence). In the important special case when dispersion is measured using the mean absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors.

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“…Whereas subgroup discovery [17] is well-studied in general [2], [4], [8], [12], [19], [24], to the best of our knowledge i end ← min{π * 1 , n 1 (Q)}; 10 for i from i beg to i end do…”

Section: Related Workmentioning

confidence: 99%

Efficiently Discovering Locally Exceptional Yet Globally Representative Subgroups

Kalofolias

Boley

Vreeken

2017

2017 IEEE International Conference on Data Mining (ICDM)

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Abstract-Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-populations that stand out on a given target variable. That is, these subpopulations are exceptional with regard to the global distribution. In this paper we argue that in many applications, such as scientific discovery, subgroups are only useful if they are additionally representative of the global distribution with regard to a control variable. That is, when the distribution of this control variable is the same, or almost the same, as over the whole data.We formalise this objective function and give an efficient algorithm to compute its tight optimistic estimator for the case of a numeric target and a binary control variable. This enables us to use the branch-and-bound framework to efficiently discover the top-k subgroups that are both exceptional as well as representative. Experimental evaluation on a wide range of datasets shows that with this algorithm we discover meaningful representative patterns and are up to orders of magnitude faster in terms of node evaluations as well as time.

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Fast exhaustive subgroup discovery with numerical target concepts

Cited by 52 publications

References 45 publications

Sports analytics for professional speed skating

Sports analytics for professional speed skating

Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery

Efficiently Discovering Locally Exceptional Yet Globally Representative Subgroups

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