SSDM 2014 - workshop on Statistically Sound Data Mining

Even if Data Mining has its roots in Statistics, there was a long while when data miners and statisticians walked their own paths. Data miners concentrated on developing efficient algorithms that addressed the practical issues associated with huge data sets, but in doing so may sometimes have paid less attention to the reliability of patterns or even their utility. On the other hand, statisticians continued on their traditional line offering well-founded and sound methods for validating statistically meaningful patterns, but they could not offer computational means to find them. Fortunately, the situation is now changing and both data miners and statisticians are recognizing the need for cooperation.
The main impetus for this new trend is coming from a third party, the application fields. In the computerized world, it is easy to collect large data sets but their analysis is more difficult. Knowing the traditional statistical tests is no more sufficient for scientists, because one should first find the most promising hidden patterns and models to be tested. This means that there is an urgent need for efficient data mining algorithms which are able to find desired patterns, without missing any significant discoveries or producing too many spurious ones. A related problem is to find a statistically justified compromise between underfitted (too generic to catch all important aspects) and overfitted (too specific, holding just due to chance) patterns. However, before any algorithms can be designed, one should first solve many principal problems, like how to define the statistical significance of desired patterns, how to evaluate overfitting, how to interprete the p-values when multiple patterns are tested, and so on. In addition, one should evaluate the existing data mining methods, alternative algorithms and goodness measures to see which of them produce statistically valid results.
As we can see, there are many important problems which should be worked together with people from Data mining, Machine learning, and Statistics as well as application fields. The goal of this workshop is to offer a meeting point for this discussion. We want bring together people from different backgrounds and schools of science, both theoretically and practically oriented, to specify problems, share solutions and brainstorm new ideas.
To encourage real workshopping of actual problems, the workshop is arranged in a novel way, containing an invited lecture and inspiring groupworks in addition to traditional presentations. This means that also the non-author participants can contribute to workshop results and even prepare a journal paper afterwards.
Topics of Interest
Topics of interest include but are not limited to:
Useful and relevant theoretical results
Search methods for statistically valid patterns and models
Statistical validation of discovered patterns
Evaluating statistical significance of clustering
Statistical techniques for avoiding overfitted patterns
Scaling statistical techniques to high-dimensionality and high data quantity, covering both theoretical problems (like multiple testing problem) and computational problems (calculating required test measures efficiently)
Interesting applications with real world data demonstrating statistically sound data mining
Empirical comparisons between between different statistical validation methods and possibly other goodness measures
Insightful positition papers
We particularly encourage submissions which compare different schools of statistics, like frequentist (Neyman-Pearsonian or Fisherian) vs. Bayesian, or analytic vs. empirical significance testing. Equally interesting are submissions introducing generic school-independent computational methods. You can also submit papers describing works-in-progress.
Organization
Workshop Chairs
Wilhelmiina Hämäläinen, Academy of Finland/School of Computing, University of Eastern Finland, Finland.
whamalai (at) cs (dot) uef (dot) fi
François Petitjean, Faculty of Information Technology, Monash University, Australia.
firstname.lastname-AT-monash.edu
Geoff Webb, Faculty of Information Technology, Monash University, Australia.
firstname.lastname-AT-monash.edu
Programme Committee
In addition to the workshop organizers:
More coming!
Niall Adams, Imperial College London, UK
Peter Flach, University of Bristol, UK
Stephane Lallich, Université Lyon 2, France
Daniel Lawson, University of Bristol, UK
Jiuyong Li, University of Southern Australia
Cécile Low-Kam, Montreal Heart Institute, Canada
Siegfried Nijssen, KU Leuven, Belgium
Chedy Raissi, INRIA, France
Jan Ramon, KU Leuven, Belgium
Nikolaj Tatti, Aalto University, Finland.