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In this contribution we introduce a clustering scheme based on mode boundary detection procedures. Modes are characterized as compact regions of the data space with higher densities than their surrounding. A mode boundary as defined in this approach is an area of large local changes in the probability density functions. Examples of the performance of the clustering based on the so-obtained mode boundaries are given using artificially generated data sets.