December 2009, Whistler, Canada
Submission deadline: Friday October 30th, 2009
Shai Ben-David, Ulrike von Luxburg, Avrim Blum, Isabelle Guyon, Robert C. Williamson, Reza Bosagh Zadeh, Margareta Ackerman
Topic of the workshop:
Clustering is one of the most widely used techniques for exploratory data analysis. In the past five decades, many clustering algorithms have been developed and applied to a wide range of practical problems. However, in spite of the abundance of clustering research published every year, we are only beginning to understand some of the most basic issues in clustering. Even though there exist many claims to success, there seems to be a lack of well established methodological procedures. In particular, addressing issues that are independent of any specific clustering algorithm, objective function, or specific data generative model, is only in its infancy. The state of affairs is perhaps not dissimilar to that in computer programming at the time of Donald Knuth’s famous Turing award lecture: “It is clearly an art, but many feel that a science is possible and desirable”.
This workshop aims at initiating a dialog between theoreticians and practitioners, aiming to bridge the theory-practice gap in this area. We want to build our workshop along three main questions:
1. FROM THEORY TO PRACTICE: Which abstract theoretical characterizations / properties / statements about clustering algorithms exist that can be helpful for practitioners and should be
adopted in practice?
2. FROM PRACTICE TO THEORY: What concrete questions would practitioners like to see addressed by theoreticians? Can we identify de-facto practices in clustering in need of theoretical grounding?
Which obscure (but seemingly needed or useful) practices are in need of rationalization?
3. FROM ART TO SCIENCE: In contrast to supervised learning, where there exist rigorous methods to assess the quality of an algorithm, such standards do not exist for clustering – clustering is still
largely an art. How can we progress towards more principled approaches, including the introduction of falsifiable hypotheses and properly designed experimentation? How could one set up a clustering challenge to compare different clustering algorithms? What could be scientific standards to evaluate a clustering algorithm in a paper?
Call for Contributions:
The workshop will consist of a mix of presentations and discussions. Researchers who want to contribute should submit an extended abstract of their work by email to
nips09 at clusteringtheory.org,
at most 4 pages, pdf format, following the NIPS style guide.
*** The deadline is Friday October 30th***
The organizers will review all submissions. Notification of acceptance will be sent out by Friday November 6th.