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Data clustering is an unsupervised data analysis and data mining technique, which offers refined and more abstract views to the inherent structure of a data set by partitioning it into a number of disjoint or overlapping (fuzzy) groups. Hundreds of clustering algorithms have been developed by researchers from a number of different scientific disciplines. The intention of this report is to present a special class of clustering algorithms, namely partition-based methods. After the introduction and a review on iterative relocation clustering algorithms, a new robust partitioning-based method is presented. Also some illustrative results are presented.