Building initial partitions through sampling techniques
A variety of iterative clustering algorithms require an initial partition of a dataset as an input parameter. As a rule a good choice of the initial partition is essential for building a high quality final partition. In this note, we generate initial partitions by using small samples of the data. Numerical experiments with k means like clustering algorithms are reported.
Eur. J. Oper. Res.