Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Grouping with Directed Relationships
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
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
Intractability and clustering with constraints
Proceedings of the 24th international conference on Machine learning
Identifying and generating easy sets of constraints for clustering
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Measuring constraint-set utility for partitional clustering algorithms
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
LEARNING AND VERIFYING SAFETY CONSTRAINTS FOR PLANNERS IN A KNOWLEDGE-IMPOVERISHED SYSTEM
Computational Intelligence
A study of K-Means-based algorithms for constrained clustering
Intelligent Data Analysis
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We focus on the problem of clustering with soft instance-level constraints. Recently, the CVQE algorithm was proposed in this context. It modifies the objective function of traditional K-means to include penalties for violated constraints. CVQE was shown to efficiently produce high-quality clustering of UCI data. In this work, we examine the properties of CVQE and propose a modification that results in a more intuitive objective function, with lower computational complexity. We present our extensive experimentation, which provides insight into CVQE and shows that our new variant can dramatically improve clustering quality while reducing run time. We show its superiority in a large-scale surveillance scenario with noisy constraints.