LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Landscape of Clustering Algorithms
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
BoostCluster: boosting clustering by pairwise constraints
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Unsupervised Face Annotation by Mining the Web
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A principled and flexible framework for finding alternative clusterings
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
C-DBSCAN: Density-Based Clustering with Constraints
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Knowledge driven dimension reduction for clustering
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Some new directions in graph-based semi-supervised learning
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Value, cost, and sharing: open issues in constrained clustering
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
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
Agglomerative hierarchical clustering with constraints: theoretical and empirical results
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Active constrained clustering by examining spectral eigenvectors
DS'05 Proceedings of the 8th international conference on Discovery Science
Improving constrained clustering with active query selection
Pattern Recognition
Hierarchical confidence-based active clustering
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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In this paper we address the problem of active query selection for clustering with constraints. The objective is to determine automatically a set of user queries to define a set of must-link or cannot-link constraints. Some works on active constraint learning have already been proposed but they are mainly applied to K-Means like clustering algorithms which are known to be limited to spherical clusters, while we are interested in clusters of arbitrary sizes and shapes. The novelty of our approach relies on the use of a k-nearest neighbor graph to determine candidate constraints coupled with a new constraint utility function. Comparative experiments conducted on real datasets from machine learning repository show that our approach significantly improves the results of constraints based clustering algorithms.