Model-based exception mining for object-relational data. (English) Zbl 1433.68375

Summary: This paper develops model-based exception mining and outlier detection for the case of object-relational data. Object-relational data represent a complex heterogeneous network, which comprises objects of different types, links among these objects, also of different types, and attributes of these links. We follow the well-established exceptional model mining (EMM) framework, which has been previously applied for subgroup discovery in propositional data; our novel contribution is to develop EMM for relational data. EMM leverages machine learning models for exception mining: An object is exceptional to the extent that a model learned for the object data differs from a model learned for the general population. In relational data, EMM can therefore be used for detecting single outlier or exceptional objects. We combine EMM with state-of-the-art statistical-relational model discovery methods for constructing a graphical model (Bayesian network), that compactly represents probabilistic associations in the data. We investigate several outlierness metrics, based on the learned object-relational model, that quantify the extent to which the association pattern of a potential outlier object deviates from that of the whole population. Our method is validated on synthetic data sets and on real-world data sets about soccer and hockey matches, IMDb movies and mutagenic compounds. Compared to baseline methods, the EMM approach achieved the best detection accuracy when combined with a novel outlinerness metric. An empirical evaluation on soccer and movie data shows a strong correlation between our novel outlierness metric and success metrics: Individuals that our metric marks out as unusual tend to have unusual success.


68T05 Learning and adaptive systems in artificial intelligence
68T09 Computational aspects of data analysis and big data
Full Text: DOI arXiv


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