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This paper deals with the question whether the quality of different clustering algorithms can be compared by a general, scientifically sound procedure which is independent
of particular clustering algorithms. In our opinion, the major obstacle is the difficulty to evaluate a clustering algorithm without taking into account the context: why does the user cluster his data in the first place, and what does he want to do with the clustering afterwards? We suggest that clustering should not be treated as an application-independent mathematical problem, but should always be studied in the context of its end-use. Different techniques to evaluate clustering algorithms have to be developed for different uses of clustering. To simplify this procedure it will be useful to build a “taxonomy of clustering problems” to identify clustering applications which can be treated in a unified way.
