Abstract
Bipartite data is common in data engineering and brings unique challenges, particularly when it comes to clustering tasks that impose strong structural assumptions. This work presents an unsupervised method for assessing similarity in bipartite data. The method is based on regular equivalence in graphs and uses spectral properties of a bipartite adjacency matrix to estimate similarity in both dimensions. The method is reflexive in that similarity in one dimension informs similarity in the other. The method also uses local graph transitivities, a contribution governed by its only free parameter. Reflexive regular equivalence can be used to validate assumptions of co-similarity, which are required but often untested in co-clustering analyses. The method is robust to noise and asymmetric data, making it particularly suited for cluster analysis and recommendation in data of unknown structure. (An extended preprint of this paper is available at arxiv.org/abs/1702.04956.)
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Gerow, A., Zhou, M., Matwin, S., Shi, F. (2017). Reflexive Regular Equivalence for Bipartite Data. In: Mouhoub, M., Langlais, P. (eds) Advances in Artificial Intelligence. Canadian AI 2017. Lecture Notes in Computer Science(), vol 10233. Springer, Cham. https://doi.org/10.1007/978-3-319-57351-9_9
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DOI: https://doi.org/10.1007/978-3-319-57351-9_9
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