{"id":159525,"date":"2006-01-01T00:00:00","date_gmt":"2006-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/star-structured-high-order-heterogeneous-data-co-clustering-based-on-consistent-information-theory\/"},"modified":"2018-10-16T22:11:18","modified_gmt":"2018-10-17T05:11:18","slug":"star-structured-high-order-heterogeneous-data-co-clustering-based-on-consistent-information-theory","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/star-structured-high-order-heterogeneous-data-co-clustering-based-on-consistent-information-theory\/","title":{"rendered":"Star-Structured High-Order Heterogeneous Data Co-clustering based on Consistent Information Theory"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Heterogeneous object co-clustering has become an important research topic in data mining. In early years of this research, people mainly worked on two types of heterogeneous data (denoted by pair-wise co-clustering); while recently more and more attention was paid to multiple types of heterogeneous data (denoted by highorder co-clustering). In this paper, we studied the highorder co-clustering of objects with star-structured interrelationship, i.e., there is a central type of objects that connects the other types of objects. Actually, this case could be a very good model for many real-world applications, such as the co-clustering of Web images, their low-level visual features, and the surrounding text. We used a tripartite graph to represent the interrelationships among different objects, and proposed a consistent information theory which generates an effective algorithm to obtain the co-clusters of different types of objects. Experiments on a Web image show that our proposed algorithm is a better choice compared with previous work on heterogeneous object co-clustering.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Heterogeneous object co-clustering has become an important research topic in data mining. In early years of this research, people mainly worked on two types of heterogeneous data (denoted by pair-wise co-clustering); while recently more and more attention was paid to multiple types of heterogeneous data (denoted by highorder co-clustering). In this paper, we studied the [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"bingao","user_id":"31232"},{"type":"user_nicename","value":"tyliu","user_id":"34431"},{"type":"user_nicename","value":"wyma","user_id":"34861"}],"msr_publishername":"IEEE Computer Society","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the Sixth International Conference on Data Mining","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"Copyright \u00a9 2007 IEEE. Reprinted from IEEE Computer Society.This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint\/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.By choosing to view this document, you agree to all provisions of the copyright laws protecting it.","msr_conference_name":"Proceedings of the Sixth International Conference on Data 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