DifFUZZY: A fuzzy spectral clustering algorithm for complex data sets

Journal article

Motivation: Soft (fuzzy) clustering techniques are often used in the study of high-dimensional data sets, such as microarray and other high-throughput bioinformatics data. The most widely used method is the Fuzzy C-means algorithm (FCM), but it can present difficulties when dealing with some data sets. Results: A spectral fuzzy clustering algorithm, DifFUZZY, applicable to a larger class of clustering problems than other fuzzy clustering algorithms is developed. Examples of data sets (synthetic and real)for which this method outperforms other frequently used algorithms are presented, including two benchmark biological data sets, a genetic expression data set and a data set that contains taxonomic measurements. This method is better than traditional fuzzy clustering algorithms at handling data sets that are “curved”, elongated or those which contain clusters of different dispersion.

Authors: Cominetti, O; Matzavinos, A; Samarasinghe, S; Kulasiri, D; Maini, P

• Publication date: 2009-01-01