About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
KDD 2006
Conference paper
Unsupervised learning on K-partite graphs
Abstract
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 data sets 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. Copyright 2006 ACM.