ScalaParBiBit: scaling the binary biclustering in distributed-memory systems

Biclustering is a data mining technique that allows us to find groups of rows and columns that are highly correlated in a 2D dataset. Although there exist several software applications to perform biclustering, most of them suffer from a high computational complexity which prevents their use in large...

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Veröffentlicht in:Cluster computing Jg. 24; H. 3; S. 2249 - 2268
Hauptverfasser: Fraguela, Basilio B., Andrade, Diego, González-Domínguez, Jorge
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.09.2021
Springer Nature B.V
Schlagworte:
Algorithms
Applications programs
Binary data
Bioinformatics
Clustering
Computer Communication Networks
Computer Science
Data compression
Data mining
Datasets
Distributed memory
Gene expression
Operating Systems
Processor Architectures
Multicore clusters
Biclustering
High performance computing
Master–slave
MPI
ISSN:1386-7857, 1573-7543
Online-Zugang:Volltext
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Zusammenfassung:Biclustering is a data mining technique that allows us to find groups of rows and columns that are highly correlated in a 2D dataset. Although there exist several software applications to perform biclustering, most of them suffer from a high computational complexity which prevents their use in large datasets. In this work we present ScalaParBiBit , a parallel tool to find biclusters on binary data, quite common in many research fields such as text mining, marketing or bioinformatics. ScalaParBiBit takes advantage of the special characteristics of these binary datasets, as well as of an efficient parallel implementation and algorithm, to accelerate the biclustering procedure in distributed-memory systems. The experimental evaluation proves that our tool is significantly faster and more scalable that the state-of-the-art tool ParBiBit in a cluster with 32 nodes and 768 cores. Our tool together with its reference manual are freely available at https://github.com/fraguela/ScalaParBiBit .
Bibliographie:ObjectType-Article-1
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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-021-03261-z