Exploring metaheuristics in a ssFCM Clustering Framework
EXPLORING METAHEURISTICS IN A SEMISUPERVISED FUZZY C-MEANS (ssFCM) CLUSTERING FRAMEWORK
- Aims : An intelligent system is required to select the most suitable techniques for automation of processes within the ssFCM framework.
- Duration: 4 yrs [2015-2018] (Extend to Evo Comp)
- Source and Amount of Funding: UBD, BND 9,231
- Collaborator’s Name: Prof Jon Garibaldi, U. Nottingham, UK; Prof Yuji Sato, Hosei U., Japan, Prof Eran Edirisinghe, Loughborough U, UK;
- Number of Publications – 3 conference papers, 1 journal paper (IMF 2019: 3.050)
- Link: [RG Project link]


