Evolutionary clustering of relational data.

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 (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of clusters in relational data. The computational complexities of the algorithms are discussed and an extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed.