Edit Event: SSDM 2017

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==Motivation and objectives== Following on from the previous successful ECML/PKDD workshop SSDM'14 we will again bring together researchers in this significant and topical field. The field of statistics has developed sophisticated, well-founded methods for inference from data. While some of these place computational or practical limits that make them infeasible to apply directly to many data mining problems, the field of data mining has much to gain from a more sophisticated understanding of the strengths and limitations of these techniques and from greater utilization of them where they are appropriate. As an answer to this dilemma, there is emerging a clear trend towards statistically sound data mining. 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 to 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. If you have relevant problems which you would like to be worked together in the workshop, please send them before the workshop. ==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 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 position 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.

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