MACs: Multi-Attribute Co-clusters with High Correlation Information
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Semi-supervised Document Clustering with Simultaneous Text Representation and Categorization
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
High-order co-clustering text data on semantics-based representation model
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
On context-aware co-clustering with metadata support
Journal of Intelligent Information Systems
A probabilistic diffusion scheme for anomaly detection on smartphones
WISTP'10 Proceedings of the 4th IFIP WG 11.2 international conference on Information Security Theory and Practices: security and Privacy of Pervasive Systems and Smart Devices
Multimedia news digger on emerging topics from social streams
Proceedings of the 20th ACM international conference on Multimedia
Social recommendation across multiple relational domains
Proceedings of the 21st ACM international conference on Information and knowledge management
Parameter-less co-clustering for star-structured heterogeneous data
Data Mining and Knowledge Discovery
A survey on enhanced subspace clustering
Data Mining and Knowledge Discovery
Social event detection with robust high-order co-clustering
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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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.