Redescription Mining

Esther Galbrun (1) , Pauli Miettinen (2)

1 ORPAILLEUR - Knowledge representation, reasonning
2 Max Planck Institute for Informatics [Saarbrücken]

This book provides a gentle introduction to redescription mining, a versatile data mining tool that is useful to find distinct common characterizations of the same objects and, vice versa, to identify sets of objects that admit multiple shared descriptions. It is intended for readers who are familiar with basic data analysis techniques such as clustering, frequent itemset mining, and classification. Redescription mining is defined in a general way, making it applicable to different types of data. The general framework is made more concrete through many practical examples that show the versatility of redescription mining. The book also introduces the main algorithmic ideas for mining redescriptions, together with applications from various domains. The final part of the book contains variations and extensions of the basic redescription mining problem, and discusses some future directions and open questions.

Mots clés: rule learning; formal concept analysis; association rule mining; decision tree; redescription mining; alternative characterizations; data mining; exploratory data analysis; multi-view data analysis; interpretable patterns; visualizations; classification; subgroup discovery; storytelling; relational data

Domaines: Intelligence artificielle [cs.AI]

Esther Galbrun, Pauli Miettinen. Redescription Mining. Springer, Cham, 2017, SpringerBriefs in Computer Science, 978-3-319-72889-6. ⟨10.1007/978-3-319-72889-6⟩. ⟨hal-01726072⟩