You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Discovering patterns that strongly distinguish one class label from another is a challenging data-mining task. The unsupervised discovery of such patterns would enable the construction of intelligible classifiers and to elicit interesting hypotheses from the data. Subgroup Discovery (SD) is one framework that formally defines this pattern mining task. However, SD still faces two major issues: (i) how to define appropriate quality measures to characterize the uniqueness of a pattern; (ii) how to select an accurate heuristic search technique when exhaustive enumeration of the pattern space is unfeasible. The first issue has been tackled by the Exceptional Model Mining (EMM) framework. This general framework aims to find patterns that cover tuples that locally induce a model that substantially differs from the model of the whole dataset. The second issue has been studied in SD and EMM mainly with the use of beam-search strategies and genetic algorithms for discovering a pattern set that is non-redundant, diverse and of high quality. In this article, we argue that the greedy nature of most of these approaches produce pattern sets that lack of diversity. Consequently, we propose to formally define pattern mining as a single-player game, as in a puzzle, and to solve it with a Monte Carlo Tree Search (MCTS), a recent technique mainly used for artificial intelligence and planning problems. The exploitation exploration trade-off and the power of random search of MCTS lead to an approach which tends towards an exhaustive search if given enough time and memory. Given a reasonable time and memory budget, MCTS quickly drives the search towards a diverse pattern set of high quality. MCTS does not need any knowledge of the pattern quality measure, and we show to what extent it is agnostic to the pattern language. We assess our claims with an exhaustive set of experiments.

Cited Abstract(s):
[1]: An exercise assembly with parallel and spaced upright posts, a grip rod spanning the posts and having the opposite end portions movably connected to the posts by means of hollow joints each having a vertical lower pipe section movably receiving the associated end portion of the grip rod and a horizontal pipe section, said opposite end portions of the grip rod having bulges provided with slanted elliptical holes, operation cylinders disposed within said horizontal pipe sections and receiving the opposite end portions of the grip rod, first connector rods received in said operation cylinders and slanted elliptical holes and second connector rods connecting said operation cylinders to said horizontal pipe sections of the joints.

[2]: We consider the problem of finding statistically unusual subgroups in a multi-relation database, and extend previous work on single-relation subgroup discovery. We give a precise definition of the multi-relation subgroup discovery task, propose a specific form of declarative bias based on foreign links as a means of specifying the hypothesis space, and show how propositional evaluation functions can be adapted to the multi-relation setting. We then describe an algorithm for this problem setting that uses optimistic estimate and minimal support pruning, an optimal refinement operator and sampling to ensure efficiency and can easily be parallelized.

[3]: This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and subgroup discovery (SD) in a unifying framework named supervised descriptive rule discovery. While all these research areas aim at discovering patterns in the form of rules induced from labeled data, they use different terminology and task definitions, claim to have different goals, claim to use different rule learning heuristics, and use different means for selecting subsets of induced patterns. This paper contributes a novel understanding of these subareas of data mining by presenting a unified terminology, by explaining the apparent differences between the learning tasks as variants of a unique supervised descriptive rule discovery task and by exploring the apparent differences between the approaches. It also shows that various rule learning heuristics used in CSM, EPM and SD algorithms all aim at optimizing a trade off between rule coverage and precision. The commonalities (and differences) between the approaches are showcased on a selection of best known variants of CSM, EPM and SD algorithms. The paper also provides a critical survey of existing supervised descriptive rule discovery visualization methods.

[4]: Exceptional model mining has been proposed as a variant of subgroup discovery especially focusing on complex target concepts. Currently, efficient mining algorithms are limited to heuristic (non exhaustive) methods. In this paper, we propose a novel approach for fast exhaustive exceptional model mining: We introduce the concept of valuation bases as an intermediate condensed data representation, and present the general GP-growth algorithm based on FP-growth. Furthermore, we discuss the scope of the proposed approach by drawing an analogy to data stream mining and provide examples for several different model classes. Runtime experiments show improvements of more than an order of magnitude in comparison to a naive exhaustive depth-first search.

[5]: Two-view datasets are datasets whose attributes are naturally split into two sets, each providing a different view on the same set of objects. We introduce the task of finding small and non-redundant sets of associations that describe how the two views are related. To achieve this, we propose a novel approach in which sets of rules are used to translate one view to the other and vice versa. Our models, dubbed translation tables , contain both unidirectional and bidirectional rules that span both views and provide lossless translation from either of the views to the opposite view. To be able to evaluate different translation tables and perform model selection, we present a score based on the Minimum Description Length (MDL) principle. Next, we introduce three Translator algorithms to find good models according to this score. The first algorithm is parameter-free and iteratively adds the rule that improves compression most. The other two algorithms use heuristics to achieve better trade-offs between runtime and compression. The empirical evaluation on real-world data demonstrates that only modest numbers of associations are needed to characterize the two-view structure present in the data, while the obtained translation rules are easily interpretable and provide insight into the data.

[6]:
SD aims at extracting subgroups of individuals for which the distribution on the target variable is statistically different from the whole (or the rest of the) population [ [1] [2] ]. Two similar notions have been formalized independently and then unified by [3] : Contrast set mining and emerging patterns. Close to SD, redescription mining aims to discover redescriptions of the same group of objects in different views [5] . Exceptional model mining (EMM) was first introduced by [6] (see a comprehensive survey by [4] ). EMM generalizes SD dealing with more complex target concepts: There are not necessarily one but several target variables to discriminate. EMM seeks to elicit patterns whose extents induce a model that substantially deviates from the one induced by the whole dataset.