Reference : Metaheuristic Based Clustering Algorithms for Biological Hypergraphs

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/31867

Title :	Metaheuristic Based Clustering Algorithms for Biological Hypergraphs
Language :	English
Author, co-author :	Changaival, Boonyarit [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Danoy, Grégoire [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Ostaszewski, Marek [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
	Lavangnananda, Kittichai [King Mongkut’s University of Technology Thonburi (Bangkok) > School of Information Technology]
	Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	27-Oct-2016
Main document title :	Proceedings of META’2016, 6th International Conference on Metaheuristics and Nature Inspired computing
Pages :	364-366
Peer reviewed :	Yes
Audience :	International
Event name :	International Conference on Metaheuristics and Nature Inspired Computing
Event date :	27-10-2016 to 31-10-2016
Event organizer :	E-G. Talbi
Event place (city) :	Marrakech
Event country :	Morocco
Keywords :	[en] Combinatorial Optimisation ; Linear Programming
Abstract :	[en] Hypergraphs are widely used for modeling and representing relationships between entities, one such field where their application is prolific is in bioinformatics. In the present era of big data, sizes and complexity of these hypergraphs grow exponentially, it is impossible to process them manually or even visualize their interconnectivity superficially. A common approach to tackle their complexity is to cluster similar data nodes together in order to create a more comprehensible representation. This enables similarity discovery and hence, extract hidden knowledge within the hypergraphs. Several state-of-the-art algorithms have been proposed for partitioning and clustering of hypergraphs. Nevertheless, several issues remain unanswered, improvement to existing algorithms are possible, especially in scalability and clustering quality. This article presents a concise survey on hypergraph-clustering algorithms with the emphasis on knowledge-representation in systems biomedicine. It also suggests a novel approach to clustering quality by means of cluster-quality metrics which combines expert knowledge and measurable objective distances in existing biological ontology.
Permalink :	http://hdl.handle.net/10993/31867
Other URL :	https://meta2016.sciencesconf.org/data/pages/Proceedings.pdf

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