Outliers on Concept Lattices

  • Mahito Sugiyama
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8417)


Outlier detection in mixed-type data, which contain both discrete and continuous features, is still a challenging problem. Here we newly introduce concept-based outlierness, which is defined on a hierarchy of clusters of data points and features, called the concept lattice, obtained by formal concept analysis (FCA). Intuitively, this outlierness is the degree of isolation of clusters on the hierarchy. Moreover, we investigate discretization of continuous features to embed the original continuous (Euclidean) space into the concept lattice. Our experiments show that the proposed method which detects concept-based outliers is more effective than other popular distance-based outlier detection methods that ignore the discreteness of features and do not take cluster relationships into account.


Outlier Formal concept analysis Concept lattice Cluster Discretization 



This work is supported by the Alexander von Humboldt Foundation.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Machine Learning and Computational Biology Research GroupMax Planck Institute for Intelligent Systems and Max Planck Institute for Developmental BiologyTübingenGermany

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