TY - GEN
T1 - Star-structured high-order heterogeneous data co-clustering based on consistent information theory
AU - Gao, Bin
AU - Liu, Tie-Yan
AU - Ma, Wei-Ying
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2006
Y1 - 2006
N2 - 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 high-order co-clustering). In this paper, we studied the high-order 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. © 2006 IEEE.
AB - 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 high-order co-clustering). In this paper, we studied the high-order 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. © 2006 IEEE.
UR - https://www.scopus.com/pages/publications/47949102777
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-47949102777&origin=recordpage
U2 - 10.1109/ICDM.2006.154
DO - 10.1109/ICDM.2006.154
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0769527019
SN - 9780769527017
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 880
EP - 884
BT - Proceedings - Sixth International Conference on Data Mining, ICDM 2006
T2 - 6th International Conference on Data Mining, ICDM 2006
Y2 - 18 December 2006 through 22 December 2006
ER -