document: Unsupervised learning on k-partite graphs Various data mining applications involve data objects of multiple types that are related to each other , which can be naturally formulated as a k-partite graph . However , the research on mining the hidden structures from a k-partite graph is still limited and preliminary . In this paper , we propose a general model , the relation summary network , to find the hidden structures ( the local cluster structures and the global community structures ) from a k-partite graph . The model provides a principal framework for unsupervised learning on k-partite graphs of various structures . Under this model , we derive a novel algorithm to identify the hidden structures of a k-partite graph by constructing a relation summary network to approximate the original k-partite graph under a broad range of distortion measures . Experiments on both synthetic and real datasets demonstrate the promise and effectiveness of the proposed model and algorithm . We also establish the connections between existing clustering approaches and the proposed model to provide a unified view to the clustering approaches .
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