Discovering Co-Cluster Structure from Relationships between Biased Objects

Iku OHAMA  Takuya KIDA  Hiroki ARIMURA  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.12   pp.3108-3122
Publication Date: 2018/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7195
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
relational data,  co-clustering,  infinite relational model,  biased objects,  

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Latent variable models for relational data enable us to extract the co-cluster structure underlying observed relational data. The Infinite Relational Model (IRM) is a well-known relational model for discovering co-cluster structures with an unknown number of clusters. The IRM and several related models commonly assume that the link probability between two objects depends only on their cluster assignment. However, relational models based on this assumption often lead us to extract many non-informative and unexpected clusters. This is because the cluster structures underlying real-world relationships are often blurred by biases of individual objects. To overcome this problem, we propose a multi-layered framework, which extracts a clear de-blurred co-cluster structure in the presence of object biases. Then, we propose the Multi-Layered Infinite Relational Model (MLIRM) which is a special instance of the proposed framework incorporating the IRM as a co-clustering model. Furthermore, we reveal that some relational models can be regarded as special cases of the MLIRM. We derive an efficient collapsed Gibbs sampler to perform posterior inference for the MLIRM. Experiments conducted using real-world datasets have confirmed that the proposed model successfully extracts clear and interpretable cluster structures from real-world relational data.