TY - GEN
T1 - Self-grouping multi-network clustering
AU - Ni, Jingchao
AU - Cheng, Wei
AU - Fan, Wei
AU - Zhang, Xiang
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - Joint clustering of multiple networks has been shown to be more accurate than performing clustering on individual networks separately. Many multi-view and multidomain network clustering methods have been developed for joint multi-network clustering. These methods typically assume there is a common clustering structure shared by all networks, and different networks can provide complementary information on this underlying clustering structure. However, this assumption is too strict to hold in many emerging real-life applications, where multiple networks have diverse data distributions. More popularly, the networks in consideration belong to different underlying groups. Only networks in the same underlying group share similar clustering structures. Better clustering performance can be achieved by considering such groups differently. As a result, an ideal method should be able to automatically detect network groups so that networks in the same group share a common clustering structure. To address this problem, we propose a novel method, COMCLUS, to simultaneously group and cluster multiple networks. COMCLUS treats node clusters as features of networks and uses them to differentiate different network groups. Network grouping and clustering are coupled and mutually enhanced during the learning process. Extensive experimental evaluation on a variety of synthetic and real datasets demonstrates the effectiveness of our method.
AB - Joint clustering of multiple networks has been shown to be more accurate than performing clustering on individual networks separately. Many multi-view and multidomain network clustering methods have been developed for joint multi-network clustering. These methods typically assume there is a common clustering structure shared by all networks, and different networks can provide complementary information on this underlying clustering structure. However, this assumption is too strict to hold in many emerging real-life applications, where multiple networks have diverse data distributions. More popularly, the networks in consideration belong to different underlying groups. Only networks in the same underlying group share similar clustering structures. Better clustering performance can be achieved by considering such groups differently. As a result, an ideal method should be able to automatically detect network groups so that networks in the same group share a common clustering structure. To address this problem, we propose a novel method, COMCLUS, to simultaneously group and cluster multiple networks. COMCLUS treats node clusters as features of networks and uses them to differentiate different network groups. Network grouping and clustering are coupled and mutually enhanced during the learning process. Extensive experimental evaluation on a variety of synthetic and real datasets demonstrates the effectiveness of our method.
UR - http://www.scopus.com/inward/record.url?scp=85014567529&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85014567529&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2016.10
DO - 10.1109/ICDM.2016.10
M3 - Conference contribution
AN - SCOPUS:85014567529
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1119
EP - 1124
BT - Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
A2 - Bonchi, Francesco
A2 - Domingo-Ferrer, Josep
A2 - Baeza-Yates, Ricardo
A2 - Zhou, Zhi-Hua
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Data Mining, ICDM 2016
Y2 - 12 December 2016 through 15 December 2016
ER -