Reference : Metaheuristic Based Clustering Algorithms for Biological Hypergraphs
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/31867
Metaheuristic Based Clustering Algorithms for Biological Hypergraphs
English
Changaival, Boonyarit mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Danoy, Grégoire mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Ostaszewski, Marek mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Lavangnananda, Kittichai mailto [King Mongkut’s University of Technology Thonburi (Bangkok) > School of Information Technology]
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
27-Oct-2016
Proceedings of META’2016, 6th International Conference on Metaheuristics and Nature Inspired computing
364-366
Yes
International
International Conference on Metaheuristics and Nature Inspired Computing
27-10-2016 to 31-10-2016
E-G. Talbi
Marrakech
Morocco
[en] Combinatorial Optimisation ; Linear Programming
[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.
http://hdl.handle.net/10993/31867
https://meta2016.sciencesconf.org/data/pages/Proceedings.pdf

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