Triclustering in Big Data Setting
Distributed, Parallel, and Cluster Computing
2020-10-27 v1 Machine Learning
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 the time and space complexity of the algorithms and justify their relevance. We also compare performance gain from using a distributed system and scalability.
Cite
@article{arxiv.2010.12933,
title = {Triclustering in Big Data Setting},
author = {Dmitry Egurnov and Dmitry I. Ignatov and Dmitry Tochilkin},
journal= {arXiv preprint arXiv:2010.12933},
year = {2020}
}
Comments
The paper contains an extended version of the prior work presented at the workshop on FCA in the Big Data Era held on June 25, 2019 at Frankfurt University of Applied Sciences, Frankfurt, Germany