By Toon Calders, Michelangelo Ceci, Donato Malerba

ISBN-10: 3319463063

ISBN-13: 9783319463063

ISBN-10: 3319463071

ISBN-13: 9783319463070

This booklet constitutes the court cases of the seventeenth foreign convention on Discovery technology, DS 2016, held in banff, AB, Canada in October 2015. The 30 complete papers offered including five abstracts of invited talks during this quantity have been conscientiously reviewed and chosen from 60 submissions.

The convention makes a speciality of following issues: Advances within the improvement and research of tools for locating scientific wisdom, coming from computing device studying, information mining, and clever information research, in addition to their program in a number of scientific domains.

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Read or Download Discovery Science: 19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016, Proceedings PDF

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

Sample text

This can be easily concluded by studying the PM representation of the subgroup Fig. 4. PM representation of the subgroups Season = Spring (left subgroup matrix) and Season = Autumn (right subgroup matrix) from the Algae dataset. Exceptional Preferences Mining 15 Fig. 5. 867 (subgroup matrix), with difference matrix on the right. (Fig. 4). 010647. 01058. With the Labelwise measure, we find more than 400 subgroups, the best of which is presented in Fig. 5. The PM clearly reveals the effect of the Labelwise quality measure: in the subgroup, the label a is strongly preferred over all others, while the image is much more nuanced over the whole dataset.

PLoS ONE 8(9), 09 (2013) 4. : Structure-odour relationships reviewed in the postgenomic era. Flavour Fragr. J. 30(5), 342–361 (2015) 5. : Structure-odor relationships of semisynthetic β-santalol analogs. Chem. Biodivers. 11(11), 1843–1860 (2014) 6. : Exceptional model mining - supervised descriptive local pattern mining with complex target concepts. Data Min. Knowl. Discov. 30(1), 47–98 (2016) 7. : Multi-interval discretization of continuous-valued attributes for classification learning. In: IJCAI (1993) 8.

The SD problem. t. the measure ϕ, with a support cardinality higher than a given minSupp. Limits of SD addressing Problem 1. The existing methods of SD either target only a single label at a time, or all labels together depending on the choice of the quality measure. In our application, it is required that a subgroup could characterize several labels at the same time. Only the WKL measure can achieve this goal [6]. However, it suffers of the curse of dimensionality: in presence of a large number of labels (74 odors in our experiments), the subgroups cannot characterize a small set of odors.

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Discovery Science: 19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016, Proceedings by Toon Calders, Michelangelo Ceci, Donato Malerba

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