Partitioning a set of objects into homogeneous clusters is a
fundamental operation in data mining. The operation is
needed in a number of data mining tasks, such as
unsupervised classification and data summation, as well as
segmentation of large heterogeneous data sets into smaller
homogeneous subsets that can be easily managed,
separately modelled and analysed. Clustering is a popular
approach used to implement this operation. Clustering
methods partition a set of objects into clusters such that
objects in the same cluster are more similar to each other
than objects in different clusters according to some defined
criteria. Statistical clustering methods (Anderberg 1973,
Jain and Dubes 1988) use similarity measures to partition
objects whereas conceptual clustering methods cluster
objects according to the concepts objects carry (Michalski
and Stepp 1983, Fisher 1987).