Download e-book for iPad: Discovery Science: 19th International Conference, DS 2016, by Toon Calders, Michelangelo Ceci, Donato Malerba

By Toon Calders, Michelangelo Ceci, Donato Malerba

ISBN-10: 3319463063

ISBN-13: 9783319463063

ISBN-10: 3319463071

ISBN-13: 9783319463070

This booklet constitutes the lawsuits of the seventeenth foreign convention on Discovery technological know-how, DS 2016, held in banff, AB, Canada in October 2015. The 30 complete papers provided 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 subject matters: Advances within the improvement and research of tools for locating scientific wisdom, coming from computing device studying, facts mining, and clever info research, in addition to their software in a number of scientific domains.

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

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Fig. 6). The Pairwise measure shows its potential on the Cpu-small dataset by identifying a subgroup with strong exceptional preferences with respect to the pair of labels a and d. As we argued in Sect. 3, one of the main benefits of a local pattern mining method such as EPM is that it delivers interpretable results. That means that the resulting subgroups are ideally suited to instigate real-world policies and actions. However, due to the employed preprocessing in the KEBI datasets (cf. Sect. 1), interpretation of results on those datasets falters.

A PM is compiled for the entire dataset, and for each subgroup under consideration. A subgroup whose PM deviates significantly from the PM for the whole dataset is then considered to be interesting. We define three quality measures for EPM that instantiate this concept of ‘interesting’ to different levels of granularity. The Norm quality measure deems a subgroup interesting if the full set of preference relations is substantially displaced. The Labelwise quality measure highlights subgroups where any one label interacts exceptionally with the other labels, agnostic of how those other labels interact with each other.

One such example is that during Spring, the types of algae a, b and c are much more common in rivers than the others. 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.

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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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