Integrating meta-path selection with user-guided object clustering in heterogeneous information networks

Real-world, multiple-typed objects are often interconnected, forming heterogeneous information networks. A major challenge for link-based clustering in such networks is its potential to generate many different results, carrying rather diverse semantic meanings. In order to generate desired clustering, we propose to use meta-path, a path that connects object types via a sequence of relations, to control clustering with distinct semantics. Nevertheless, it is easier for a user to provide a few examples (“seeds”) than a weighted combination of sophisticated meta-paths to specify her clustering preference. Thus, we propose to integrate meta-path selection with user-guided clustering to cluster objects in networks, where a user first provides a small set of object seeds for each cluster as guidance. Then the system learns the weights for each meta-path that are consistent with the clustering result implied by the guidance, and generates clusters under the learned weights of meta-paths...

• Type: Journal
• Year: 2012
• Where: KDD
• Authors: Yizhou Sun, Brandon Norick, Jiawei Han, Xifeng Yan, Philip S. Yu, Xiao Yu
• Keywords: Army Research Laboratory, Data Mining, KDD 2012, Path Selection, U S Air Force