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Erschienen in:
Formal Concept Analysis and Extensions for Complex Data Analytics Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

11. Triclustering in Big Data Setting

verfasst von : Dmitry Egurnov, Dmitry I. Ignatov, Dmitry Tochilkin

Erschienen in: Complex Data Analytics with Formal Concept Analysis

Verlag: Springer International Publishing

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Abstract

In this paper, we describe versions of triclustering algorithms adapted for efficient calculations in distributed environments with MapReduce model or parallelisation mechanism provided by modern programming languages. OAC-family of triclustering algorithms shows good parallelisation capabilities due to the independent processing of triples of a triadic formal context. We provide time and space complexity of the algorithms and justify their relevance. We also compare performance gain from using a distributed system and scalability.

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Vorheriges Kapitel Towards Distributivity in FCA for Phylogenetic Data
Fußnoten
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Metadaten
Titel
Triclustering in Big Data Setting
verfasst von
Dmitry Egurnov
Dmitry I. Ignatov
Dmitry Tochilkin
Copyright-Jahr
2022
Buch
Complex Data Analytics with Formal Concept Analysis

Print ISBN: 978-3-030-93277-0

Electronic ISBN: 978-3-030-93278-7

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-93278-7_11