Metaheuristic Based Clustering Algorithms for Biological Hypergraphs
Boonyarit Changaivalboonyarit.changaival@uni.lu
Gregoire Danoygregoire.danoy@uni.lu
Marek Ostaszewskimarek.ostaszewski@uni.lu
Kittichai Lavangnanandakitt@sit.kmutt.ac.th
Franck Leprevost
Pascal Bouvrypascal.bouvry@uni.lu
Computer Science and Communications Research Unit, University of Luxembourg, Luxembourg City, Luxembourg
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand
Hypergraphs are widely used for modeling and representing relationships between entities, one such field where their application is prolific is in bioinformatics [1] and [2]. 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 hyper graphs. 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.
combinatorial optimisation
linear programming