Fast evolutionary algorithms for relational clustering

This paper is concerned with the computational efficiency of clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. Two relational versions of an evolutionary algorithm for clustering are derived and compared against two systematic (repetitive) approaches that can also be used to automatically estimate the number of clusters in relational data. Exhaustive experiments involving six artificial and two real data sets are reported and analyzed