Clustering: Science or Art?


Conference Paper


We examine whether the quality of di erent clustering algorithms can be compared by a general, scienti cally 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 rst 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. Di erent techniques to evaluate clustering algorithms have to be developed for di erent 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 uni ed way and that such an e 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.

Author(s): von Luxburg, U. and Williamson, R. and Guyon, I.
Book Title: JMLR Workshop and Conference Proceedings, Volume 27
Pages: 65-79
Year: 2012
Day: 0

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Digital: 0
Event Name: Workshop on Unsupervised Learning and Transfer Learning
Event Place: Bellevue, Washington, USA
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Clustering: Science or Art?},
  author = {von Luxburg, U. and Williamson, R. and Guyon, I.},
  booktitle = {JMLR Workshop and Conference Proceedings, Volume 27},
  pages = {65-79},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2012}