DifFUZZY: a fuzzy clustering algorithm for complex datasets

Authors:
Ornella Cominetti;Anastasios Matzavinos;Sandhya Samarasinghe;Don Kulasiri;Sijia Liu;Philip K. Maini;Radek Erban
Affiliations:
Centre for Mathematical Biology, Mathematical Institute, University of Oxford, 24-29 St. Giles;, Oxford, OX1 3LB, UK.;Department of Mathematics, Iowa State University, Ames, IA 50011, USA.;Centre for Advanced Computational Solutions (C-fACS), Lincoln University, P.O. Box 84, Christchurch, New Zealand.;Centre for Advanced Computational Solutions (C-fACS), Lincoln University, P.O. Box 84, Christchurch, New Zealand.;Department of Mathematics, Iowa State University, Ames, IA 50011, USA.;Centre for Mathematical Biology, Mathematical Institute, University of Oxford, 24-29 St. Giles
Venue:
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Year:
2010

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Abstract

Soft (fuzzy) clustering techniques are often used in the study of high-dimensional datasets, such as microarray and other high-throughput bioinformatics data. The most widely used method is the fuzzy C-means (FCM) algorithm, but it can present difficulties when dealing with some datasets. A fuzzy clustering algorithm, DifFUZZY, which utilises concepts from diffusion processes in graphs and is applicable to a larger class of clustering problems than other fuzzy clustering algorithms is developed. Examples of datasets (synthetic and real) for which this method outperforms other frequently used algorithms are presented, including two benchmark biological datasets, a genetic expression dataset and a dataset that contains taxonomic measurements. This method is better than traditional fuzzy clustering algorithms at handling datasets that are 'curved', elongated or those which contain clusters of different dispersion. The algorithm has been implemented in Matlab and C++ and is available at http://www.maths.ox.ac.uk/cmb/difFUZZY.