Measuring the Implications of the D-Basis in Analysis of Data in Biomedical Studies

Adaricheva, Kira; Nation, J. B.; Okimoto, Gordon; Adarichev, Vyacheslav A.; Amanbekkyzy, Adina; Sarkar, Shuchismita; Sailanbayev, Alibek; Seidalin, Nazar; Alibek, Ken

doi:10.1007/978-3-319-19545-2_3

Cited by 4 publications

(10 citation statements)

References 14 publications

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“…Recently, U. Russel et al [32] proposed a method of retrieval of the canonical direct unit basis that would employ the hypergraph dualization algorithm. While the actual coding applied some back-tracking and simple heuristics instead of actual implementation of dualization, the computer test results showed considerably shorter running times when compared to existing FCA implementations: on a particular data table of size 26 × 79 the new algorithm would return the basis of 86 1 We mention that some efforts were spent on parallelization of algorithms building a Galois lattice from the binary table, such as in P. Krajca et al [27], but possible utilization of these approaches for computation of pseudo-closed sets has not yet been developed. implications in 0.1 sec compared to 6.5 hours of the FCA implementation, and, on random tables of size 20 × 40, it spent about 1/50 of time needed for the FCA algorithm.…”

Section: 2mentioning

confidence: 99%

Discovery of the D-basis in binary tables based on hypergraph dualization

Adaricheva¹,

Nation²

2017

Theoretical Computer Science

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Abstract. Discovery of (strong) association rules, or implications, is an important task in data management, and it finds application in artificial intelligence, data mining and the semantic web. We introduce a novel approach for the discovery of a specific set of implications, called the D-basis, that provides a representation for a reduced binary table, based on the structure of its Galois lattice. At the core of the method are the D-relation defined in the lattice theory framework, and the hypergraph dualization algorithm that allows us to effectively produce the set of transversals for a given Sperner hypergraph. The latter algorithm, first developed by specialists from Rutgers Center for Operations Research, has already found numerous applications in solving optimization problems in data base theory, artificial intelligence and game theory. One application of the method is for analysis of gene expression data related to a particular phenotypic variable, and some initial testing is done for the data provided by the

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Section: 2mentioning

confidence: 99%

Discovery of the D-basis in binary tables based on hypergraph dualization

Adaricheva¹,

Nation²

2017

Theoretical Computer Science

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“…Many options are provided to conveniently display a set of rules as gene regulatory network and to assess the quality of rules with different interestingness measures. Interestingly, other applications such as [3] exist for data analysis in biomedical studies with implications, for which RQL could also be useful.…”

Section: Related Workmentioning

confidence: 99%

RQL: A Query Language for Rule Discovery in Databases

Chardin

Coquery

Pailloux

et al. 2017

Theoretical Computer Science

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“…In this paper, we expand the approach developed in [2] of the extraction of implications and ranking of attributes with respect to a target attribute.…”

Section: Introductionmentioning

confidence: 99%

“…Having fixed ∆(b) and ∆(¬b), we then used the approach described in [2] to rank the attributes by the relevance parameter.…”

Section: Introductionmentioning

confidence: 99%

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The Bases of Association Rules of High Confidence

Segal¹,

Cabot-Miller²,

Adaricheva³

et al. 2018

Computer Science &Amp; Information Technology (CS &Amp; IT)

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We develop a new approach for distributed computing of the association rules of high confidence in a binary table. It is derived from the D-basis algorithm [1], which is performed on multiple sub-tables of a table given by removing several rows at a time. The set of rules is then aggregated using the same approach as the D-basis is retrieved from a larger set of implications. This allows to obtain a basis of association rules of high confidence, which can be used for ranking all attributes of the table with respect to a given fixed attribute using the relevance parameter introduced in [2]. This paper focuses on the technical implementation of the new algorithm. Some testing results are performed on transaction data and medical data.

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Measuring the Implications of the D-Basis in Analysis of Data in Biomedical Studies

Cited by 4 publications

References 14 publications

Discovery of the D-basis in binary tables based on hypergraph dualization

Discovery of the D-basis in binary tables based on hypergraph dualization

RQL: A Query Language for Rule Discovery in Databases

The Bases of Association Rules of High Confidence

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