A single-pass triclustering algorithm

Gnatyshak, Dmitry

doi:10.3103/s0005105515010057

Cited by 5 publications

(3 citation statements)

References 27 publications

(26 reference statements)

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“…Finally, following the increasing popularity of the big data field, a novel, efficient algorithm, in terms of time and memory, with reduced computational cost, was presented in [29]. The authors also affirmed that the algorithm can be easily parallelized to become adapted to the new big data programming paradigms.…”

Section: Use Of Triclusteringmentioning

confidence: 92%

A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula

Martínez‐Álvarez

Gutiérrez‐Avilés

Morales‐Esteban

et al. 2015

Entropy

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A previous definition of seismogenic zones is required to do a probabilistic seismic hazard analysis for areas of spread and low seismic activity. Traditional zoning methods are based on the available seismic catalog and the geological structures. It is admitted that thermal and resistant parameters of the crust provide better criteria for zoning. Nonetheless, the working out of the rheological profiles causes a great uncertainty. This has generated inconsistencies, as different zones have been proposed for the same area. A new method for seismogenic zoning by means of triclustering is proposed in this research. The main advantage is that it is solely based on seismic data. Almost no human decision is made, and therefore, the method is nearly non-biased. To assess its performance, the method has been applied to the Iberian Peninsula, which is characterized by the occurrence of small to moderate magnitude earthquakes. The catalog of the National Geographic Institute of Spain has been used. The output map is checked for validity with the geology. Moreover, a Entropy 2015, 17 5001 geographic information system has been used for two purposes. First, the obtained zones have been depicted within it. Second, the data have been used to calculate the seismic parameters (b-value, annual rate). Finally, the results have been compared to Kohonen's self-organizing maps.

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Section: Use Of Triclusteringmentioning

confidence: 92%

A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula

Martínez‐Álvarez

Gutiérrez‐Avilés

Morales‐Esteban

et al. 2015

Entropy

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“…More recently, Tribox, SpecTric, OAC-triclustering (based on primes of pairs), and Krimp-triclustering were proposed to overcome some of the drawbacks of previous algorithms, such as pattern explosion and quality Mirkin and Kramarenko 2011;Yurov and Ignatov 2017). As primebased operators satisfy single-pass and linearity search conditions, OAC-triclustering was recently extended within a new algorithm, here referred to as Online OAC, that further explores efficiency gains and can be parallelized (Gnatyshak 2015). A generalization of this task for the discovery of n-ary formal concepts from n-dimensional data was proposed by Cerf et al (2009) with the Data-Peeler algorithm and later extended to tolerate noise (Cerf et al 2013).…”

Section: Exhaustive Approachesmentioning

confidence: 99%

“…Triclustering has been recently applied for the analysis of folksonomies, text, or unstructured (web) data (Gnatyshak 2015; to identify user-sensitive consensus in sourceuser-tag data and correlated content cubes measuring the frequency of categories/words across text segments.…”

Section: Other Applicationsmentioning

confidence: 99%

Triclustering Algorithms for Three-Dimensional Data Analysis

Henriques

Madeira

2018

ACM Comput. Surv.

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Three-dimensional data are increasingly prevalent across biomedical and social domains. Notable examples are gene-sample-time, individual-feature-time, or node-node-time data, generally referred to as observationattribute-context data. The unsupervised analysis of three-dimensional data can be pursued to discover putative biological modules, disease progression profiles, and communities of individuals with coherent behavior, among other patterns of interest. It is thus key to enhance the understanding of complex biological, individual, and societal systems. In this context, although clustering can be applied to group observations, its relevance is limited since observations in three-dimensional data domains are typically only meaningfully correlated on subspaces of the overall space. Biclustering tackles this challenge but disregards the third dimension. In this scenario, triclustering-the discovery of coherent subspaces within three-dimensional data-has been largely researched to tackle these problems. Despite the diversity of contributions in this field, there still lacks a structured view on the major requirements of triclustering, desirable forms of homogeneity (including coherency, structure, quality, locality, and orthonormality criteria), and algorithmic approaches. This work formalizes the triclustering task and its scope, introduces a taxonomy to categorize the contributions in the field, provides a comprehensive comparison of state-of-the-art triclustering algorithms according to their behavior and output, and lists relevant real-world applications. Finally, it highlights challenges and opportunities to advance the field of triclustering and its applicability to complex three-dimensional data analysis.

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Artificial Intelligence Techniques Based on K-MeansTwo Way Clustering and Greedy Triclustering Approach for 3D Gene Expression Data (GED)

Narmadha¹,

Rathipriya

2023

Medical Virology: From Pathogenesis to Disease Control

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A single-pass triclustering algorithm

Cited by 5 publications

References 27 publications

A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula

A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula

Triclustering Algorithms for Three-Dimensional Data Analysis

Artificial Intelligence Techniques Based on K-MeansTwo Way Clustering and Greedy Triclustering Approach for 3D Gene Expression Data (GED)

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