Parallel algorithms for hierarchical clustering
Parallel Computing
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Active semi-supervised fuzzy clustering
Pattern Recognition
Near-duplicate keyframe retrieval by nonrigid image matching
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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Constrained clustering has recently become an active research topic. This type of clustering methods takes advantage of partial knowledge in the form of pairwise constraints, and acquires significant improvement beyond the traditional unsupervised clustering. However, most of the existing constrained clustering methods use constraints which are selected at random. Recently active constrained clustering algorithms utilizing active constraints have proved themselves to be more effective and efficient. In this paper, we propose an improved algorithm which introduces multiple representatives into constrained clustering to make further use of the active constraints. Experiments on several benchmark data sets and public image data sets demonstrate the advantages of our algorithm over the referenced competitors.