DifFUZZY: a fuzzy clustering algorithm for complex datasets

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.