Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning to Probabilistically Identify Authoritative Documents
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
A divisive information theoretic feature clustering algorithm for text classification
The Journal of Machine Learning Research
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Spectral clustering for multi-type relational data
ICML '06 Proceedings of the 23rd international conference on Machine learning
Clustering with Bregman Divergences
The Journal of Machine Learning Research
A probabilistic framework for relational clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A tutorial on spectral clustering
Statistics and Computing
CrossClus: user-guided multi-relational clustering
Data Mining and Knowledge Discovery
CSV: visualizing and mining cohesive subgraphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
CONSENSUS-BASED ENSEMBLES OF SOFT CLUSTERINGS
Applied Artificial Intelligence
RankClus: integrating clustering with ranking for heterogeneous information network analysis
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Ranking-based clustering of heterogeneous information networks with star network schema
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Discriminative semi-supervised feature selection via manifold regularization
IEEE Transactions on Neural Networks
Community discovery using nonnegative matrix factorization
Data Mining and Knowledge Discovery
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Real-world, multiple-typed objects are often interconnected, forming heterogeneous information networks. A major challenge for link-based clustering in such networks is their 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 weight for each meta-path that is consistent with the clustering result implied by the guidance, and generates clusters under the learned weights of meta-paths. A probabilistic approach is proposed to solve the problem, and an effective and efficient iterative algorithm, PathSelClus, is proposed to learn the model, where the clustering quality and the meta-path weights mutually enhance each other. Our experiments with several clustering tasks in two real networks and one synthetic network demonstrate the power of the algorithm in comparison with the baselines.