The applicability of evolution strategies (ESs), population based stoc
hastic optimization techniques, to optimize clustering objective funct
ions is explored. Clustering objective functions are categorized into
centroid and non-centroid type of functions. Optimization of the centr
oid type of objective functions is accomplished by formulating them as
functions of real-valued parameters using ESs. Both hard and fuzzy cl
ustering objective functions are considered in this study. Applicabili
ty of ESs to discrete optimization problems is extended to optimize th
e non-centroid type of objective functions. As ESs are amenable to par
allelization, a parallel model (master/slave model) is described in th
e context of the clustering problem. Results obtained for selected dat
a sets substantiate the utility of ESs in clustering.