Abstract
We propose a novel approach for interactive redescription set exploration and redescription analysis realized through the tool InterSet. The tool is developed for interaction with possibly large redescription sets, produced on large datasets, and it enables better understanding of the underlying data and relations between attribute sets. New insights from redescription sets can be obtained through three different interaction modes based on: (i) similarity of entity occurrence in redescription support sets, (ii) attribute co-occurence in redescriptions and (iii) redescription quality measures. These modes provide additional contextualization, which is a major advantage compared to current state of the art approaches that allow interactive redescription set exploration, enabling users to obtain new knowledge in the form of interesting redescription subsets which can be analysed further on the level of individual redescriptions.
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Acknowledgement
The authors acknowledge the European Commissions support through the MAESTRA project (Gr. no. 612944), the MULTIPLEX project (Gr.no. 317532) and support of the Croatian Science Foundation (Pr. no. 9623: Machine Learning Algorithms for Insightful Analysis of Complex Data Structures).
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Mihelčić, M., Šmuc, T. (2016). InterSet: Interactive Redescription Set Exploration. In: Calders, T., Ceci, M., Malerba, D. (eds) Discovery Science. DS 2016. Lecture Notes in Computer Science(), vol 9956. Springer, Cham. https://doi.org/10.1007/978-3-319-46307-0_3
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