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Extra info for Discovery Science: 19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016, Proceedings

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Springer, Heidelberg (2014). 1007/978-3-319-11812-3 11 13. : Label ranking by learning pairwise preferences. Artif. Intell. 172(16–17), 1897–1916 (2008) 14. : Subgroup discovery in smart electricity meter data. IEEE Trans. Industr. Inf. R. de S´ a et al. 15. : Visual interactive subgroup discovery with numerical properties of interest. , Matwin, S. ) DS 2006. LNCS (LNAI), vol. 4265, pp. 301–305. Springer, Heidelberg (2006). 1007/ 11893318 31 16. : Nantonac collaborative filtering: recommendation based on order responses.

Problem 1 (SOR Problem). Given an olfactory dataset, the aim is to characterize and describe the relationships between the physicochemical properties of odorant molecules and their olfactory qualities. A data-science approach. Answering this problem requires experts of different domains. The odor space is related to the study of olfaction in neuroscience, the understanding of the physicochemical space requires chemical skills, and finally, exploring jointly these two spaces requires data analysis techniques from computer science.

Data Min. Knowl. Discov. 30(1), 47–98 (2016) 7. : Multi-interval discretization of continuous-valued attributes for classification learning. In: IJCAI (1993) 8. : Foundations of Rule Learning. Springer, Heidelberg (2012) 9. : From black and white to full color: extending redescription mining outside the Boolean world. Stat. Anal. Data Min. 5(4), 284–303 (2012) 34 G. Bosc et al. 10. : Odor classification: a review of factors influencing perception-based odor arrangements. Chem. Senses 38(3), 189–209 (2013) 11.

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