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Abstract

We examine whether the quality of dierent clustering algorithms can be compared by a general, scientically sound procedure which is independent of particular clustering algorithms. We argue that the major obstacle is the diculty in evaluating 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 argue that clustering should not be treated as an application-independent mathematical problem, but should always be studied in the context of its end-use. Dierent techniques to evaluate clustering algorithms have to be developed for dierent uses of clustering. To simplify this procedure we argue that it will be useful to build a \taxonomy of clustering problems" to identify clustering applications which can be treated in a unique way and that such an ort will be more fruitful than attempting the impossible | developing \optimal" domain-independent clustering algorithms or even classifying clustering algorithms in terms of how they work.