Association Discovery in Two-View Data

Leeuwen, Matthijs van; Galbrun, Esther

doi:10.1109/tkde.2015.2453159

Cited by 16 publications

(15 citation statements)

References 21 publications

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“…In data mining, the MDL principle has been used to summarize different types of data, e.g., transaction data [42,9], and two-view data [40].…”

Section: Mdl-based Data Miningmentioning

confidence: 99%

“…Equation and the complexity of that model, L(R). Note that, on a high level, two-part code in (6) is similar to the one that was recently used in the context of two-view data [40], but there the goal was summarization rather than classification and the details of the encodings are very different. The next subsections describe the two parts of the encoding, together with examples of how to compute the length of the encodings in practice.…”

Section: For Multiclass Classificationmentioning

confidence: 99%

“…Greedy search is very commonly used for learning decision trees and rule lists [35,10,15], as well as for pattern-based modelling using the MDL principle [42,9,40]. A few recent approaches use optimization techniques [46], but these have the limitation that the search space must be strongly reduced, providing an exact solution to an approximate problem (as opposed to an approximate solution to an exact problem).…”

Section: Separate-and-conquer Greedy Searchmentioning

confidence: 99%

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Interpretable multiclass classification by MDL-based rule lists

Proença

Leeuwen

2020

Information Sciences

Self Cite

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Interpretable classifiers have recently witnessed an increase in attention from the data mining community because they are inherently easier to understand and explain than their more complex counterparts. Examples of interpretable classification models include decision trees, rule sets, and rule lists. Learning such models often involves optimizing hyperparameters, which typically requires substantial amounts of data and may result in relatively large models.In this paper, we consider the problem of learning compact yet accurate probabilistic rule lists for multiclass classification. Specifically, we propose a novel formalization based on probabilistic rule lists and the minimum description length (MDL) principle. This results in virtually parameter-free model selection that naturally allows to trade-off model complexity with goodness of fit, by which overfitting and the need for hyperparameter tuning are effectively avoided. Finally, we introduce the Classy algorithm, which greedily finds rule lists according to the proposed criterion.We empirically demonstrate that Classy selects small probabilistic rule lists that outperform state-of-the-art classifiers when it comes to the combination of predictive performance and interpretability. We show that Classy is insensitive to its only parameter, i.e., the candidate set, and that compression on the training set correlates with classification performance, validating our MDL-based selection criterion.

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“…In data mining, the MDL principle has been used to summarize different types of data, e.g., transaction data [42,9], and two-view data [40].…”

Section: Mdl-based Data Miningmentioning

confidence: 99%

Section: For Multiclass Classificationmentioning

confidence: 99%

Section: Separate-and-conquer Greedy Searchmentioning

confidence: 99%

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Interpretable multiclass classification by MDL-based rule lists

Proença

Leeuwen

2020

Information Sciences

Self Cite

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“…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%

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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“…The main difference is that association rules produce one directional associations while redescription mining produces bi directional associations. Two-view data association discovery (van Leeuwen & Galbrun, 2015) aims at finding a small, non -redundant set of associations that provide insight in how two views are related. Produced associations are both uni and bi directional as opposed to redescription mining that only produces bi directional connections providing interesting descriptions of instances.…”

Section: Introductionmentioning

confidence: 99%

A framework for redescription set construction

Mihelčić

Džeroski

Lavrač

et al. 2017

Expert Systems with Applications

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Redescription mining is a field of knowledge discovery that aims at finding different descriptions of similar subsets of instances in the data. These descriptions are represented as rules inferred from one or more disjoint sets of attributes, called views. As such, they support knowledge discovery process and help domain experts in formulating new hypotheses or constructing new knowledge bases and decision support systems. In contrast to previous approaches that typically create one smaller set of redescriptions satisfying a pre-defined set of constraints, we introduce a framework that creates large and heterogeneous redescription set from which user/expert can extract compact sets of differing properties, according to its own preferences. Construction of large and heterogeneous redescription set relies on CLUS-RM algorithm and a novel, conjunctive refinement procedure that facilitates generation of larger and more accurate redescription sets. The work also introduces the variability of redescription accuracy when missing values are present in the data, which significantly extends applicability of the method. Crucial part of the framework is the redescription set extraction based on heuristic multi-objective optimization procedure that allows user to define importance levels towards one or more redescription quality criteria. We provide both theoretical and empirical comparison of the novel framework against current state of the art redescription mining algorithms and show that it represents more efficient and versatile approach for mining redescriptions from data.

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Association Discovery in Two-View Data

Cited by 16 publications

References 21 publications

Interpretable multiclass classification by MDL-based rule lists

Interpretable multiclass classification by MDL-based rule lists

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

A framework for redescription set construction

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