TY - GEN
T1 - Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering
AU - Gao, Bin
AU - Liu, Tie-Yan
AU - Zheng, Xin
AU - Cheng, Qian-Sheng
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 - 2005
Y1 - 2005
N2 - Heterogeneous data co-clustering has attracted more and more attention in recent years due to its high impact on various applications. While the co-clustering algorithms for two types of heterogeneous data (denoted by pair-wise co-clustering), such as documents and terms, have been well studied in the literature, the work on more types of heterogeneous data (denoted by high-order co-clustering) is still very limited. As an attempt in this direction, in this paper, we worked on a specific case of high-order co-clustering in which there is a central type of objects that connects the other types so as to form a star structure of the interrelationships. Actually, this case could be a very good abstract for many real-world applications, such as the co-clustering of categories, documents and terms in text mining. In our philosophy, we treated such kind of problems as the fusion of multiple pair-wise co-clustering sub-problems with the constraint of the star structure. Accordingly, we proposed the concept of consistent bipartite graph co-partitioning, and developed an algorithm based on semi-definite programming (SDP) for efficient computation of the clustering results. Experiments on toy problems and real data both verified the effectiveness of our proposed method. Copyright 2005 ACM.
AB - Heterogeneous data co-clustering has attracted more and more attention in recent years due to its high impact on various applications. While the co-clustering algorithms for two types of heterogeneous data (denoted by pair-wise co-clustering), such as documents and terms, have been well studied in the literature, the work on more types of heterogeneous data (denoted by high-order co-clustering) is still very limited. As an attempt in this direction, in this paper, we worked on a specific case of high-order co-clustering in which there is a central type of objects that connects the other types so as to form a star structure of the interrelationships. Actually, this case could be a very good abstract for many real-world applications, such as the co-clustering of categories, documents and terms in text mining. In our philosophy, we treated such kind of problems as the fusion of multiple pair-wise co-clustering sub-problems with the constraint of the star structure. Accordingly, we proposed the concept of consistent bipartite graph co-partitioning, and developed an algorithm based on semi-definite programming (SDP) for efficient computation of the clustering results. Experiments on toy problems and real data both verified the effectiveness of our proposed method. Copyright 2005 ACM.
KW - Co-clustering
KW - Consistency
KW - High-Order Heterogeneous Data
KW - Spectral Graph
UR - https://www.scopus.com/pages/publications/32344447271
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-32344447271&origin=recordpage
U2 - 10.1145/1081870.1081879
DO - 10.1145/1081870.1081879
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 41
EP - 50
BT - KDD-2005 - Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
T2 - KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Y2 - 21 August 2005 through 24 August 2005
ER -