Redescription Mining

Galbrun, Esther; Miettinen, Pauli

doi:10.1007/978-3-319-72889-6

Cited by 15 publications

(27 citation statements)

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“…PAPP-A levels of smokers were lower than that of non-smokers and there is a positive correlation between serum PAPP-A levels and subjects' age. The measured median value of PAPP-A in males 6.85 with the range [undetectable, 24,40] ng/mL were significantly higher than the median of female subjects 3.4 with the measured range [undetectable, 36,7] ng/ml. For both males and females, non-smokers had higher levels of PAPP-A than smokers.…”

Section: Discussionmentioning

confidence: 78%

Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients

et al. 2017

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Based on a set of subjects and a collection of attributes obtained from the Alzheimer’s Disease Neuroimaging Initiative database, we used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer’s disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, ciliary neurotrophic factor, brain natriuretic peptide, Fas ligand, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Moreover, applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A), which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p ≤ 0.01) were found between PAPP-A and clinical tests: Alzheimer’s Disease Assessment Scale, Clinical Dementia Rating Sum of Boxes, Mini Mental State Examination, etc. The high importance of this finding lies in the fact that PAPP-A is a metalloproteinase, known to cleave insulin-like growth factor binding proteins. Since it also shares similar substrates with A Disintegrin and the Metalloproteinase family of enzymes that act as α-secretase to physiologically cleave amyloid precursor protein (APP) in the non-amyloidogenic pathway, it could be directly involved in the metabolism of APP very early during the disease course. Therefore, further studies should investigate the role of PAPP-A in the development of AD more thoroughly.

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Section: Discussionmentioning

confidence: 78%

Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients

et al. 2017

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“…For all algorithms, we used the maximal p-value threshold of 0.01 (the strictest significance threshold). The minimal Jaccard index was set to 0.2 for the DBLP dataset based on results presented in Galbrun (2013), Table 6.1, p. 46. The same is set to 0.6 for the Bio dataset based on results in Galbrun (2013) Table 7, p. 301.…”

Section: Methodsmentioning

confidence: 99%

“…The minimal Jaccard index was set to 0.2 for the DBLP dataset based on results presented in Galbrun (2013), Table 6.1, p. 46. The same is set to 0.6 for the Bio dataset based on results in Galbrun (2013) Table 7, p. 301. The threshold 0.5 for the Country dataset was experimentally determined.…”

Section: Methodsmentioning

confidence: 99%

“…Given a dataset D, a query language Q over a set of attributes V , and a set of constraints C, the task of redescription mining (Galbrun, 2013) is to find all redescriptions satisfying constraints in C.…”

Section: Notation and Definitionsmentioning

confidence: 99%

Section: Individual Redescription Quality Measuresmentioning

confidence: 99%

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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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Using Redescriptions and Formal Concept Analysis for Mining Definitions in Linked Data

Reynaud

Toussaint

Napoli

2019

Formal Concept Analysis

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In this article, we compare the use of Redescription Mining (RM) and Association Rule Mining (ARM) for discovering class definitions in Linked Open Data (LOD). RM is aimed at mining alternate descriptions from two datasets related to the same set of individuals. We reuse RM for providing category definitions in DBpedia in terms of necessary and sufficient conditions (NSC). Implications and AR can be jointly used for mining category definitions still in terms of NSC. In this paper, we firstly, recall the basics of redescription mining and make precise the principles of definition discovery. Then we detail a series of experiments carried out on datasets extracted from DBpedia. We analyze the different outputs related to RM and ARM applications, and we discuss the strengths and limitations of both approaches. Finally, we point out possible improvements of the approaches.

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Redescription Mining

Cited by 15 publications

References 0 publications

Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients

Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients

A framework for redescription set construction

Using Redescriptions and Formal Concept Analysis for Mining Definitions in Linked Data

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