Redescription Mining: An Overview.

International audienceIn many real-world data analysis tasks, we have different types of data over the same objects or entities, perhaps because the data originate from distinct sources or are based on different terminologies. In order to understand such data, an intuitive approach is to identify thecorrespondences that exist between these different aspects. This isthe motivating principle behind redescription mining, a data analysistask that aims at finding distinct commoncharacterizations of the same objects.This paper provides a short overview of redescription mining; what it is and how it is connected to other data analysis methods; the basic principles behind current algorithms for redescription mining; and examples and applications of redescription mining for real-world data analysis problems

Analysing Political Opinions Using Redescription Mining

International audienceUnderstanding the socio-economical background of voters supporting a certain cause or, vice versa, understanding the political stance of people from a certain socio-economical niche are important questions in political sciences. Traditionally, answering these questions has required the researcher to fix either the political stance or the socio-economical background. In this paper, we propose using redescription mining to automatically find the stances and niches that correspond to each other. We show how redescription mining can be applied to open data from voting advice applications, providing insights about the position of the candidates to parliamentary elections. Furthermore, we show that these insights are not only descriptive, but that they also generalize well to new data

Mining Predictive Redescriptions with Trees

International audienceIn many areas of science, scientists need to find distinct common characterizations of the same objects and, vice versa, identify sets of objects that admit multiple shared descriptions. For example, a biologist might want to find a set of bioclimatic conditions and a set of species, such that this bioclimatic profile adequately characterizes the areas inhabited by these fauna. In data analysis, the task of automatically generating such alternative characterizations is called redescription mining. A number of algorithms have been proposed for mining redescriptions which usually differ on the type of redescriptions they construct. In this paper, we demonstrate the power of tree-based redescriptions and present two new algorithms for mining them. Tree-based redescriptions can have very strong predictive power (i.e. they generalize well to unseen data), but unfortunately they are not always easy to interpret. To alleviate this major drawback, we present an adapted visualization, integrated into an existing interactive mining framework