A survey of constrained classification
Computational Statistics & Data Analysis
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Reinterpreting the Category Utility Function
Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Clustering with Instance-level Constraints
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
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Ensembles of Partitions via Data Resampling
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Analysis of Consensus Partition in Cluster Ensemble
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
An information-theoretic external cluster-validity measure
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
Resampling-based selective clustering ensembles
Pattern Recognition Letters
A novel hierarchical-clustering-combination scheme based on fuzzy-similarity relations
IEEE Transactions on Fuzzy Systems
The multi-view information bottleneck clustering
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Learning multiple nonredundant clusterings
ACM Transactions on Knowledge Discovery from Data (TKDD)
Nonparametric Bayesian clustering ensembles
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
How to "alternatize" a clustering algorithm
Data Mining and Knowledge Discovery
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Data may often contain multiple plausible clusterings. In order to discover a clustering which is useful to the user, constrained clustering techniques have been proposed to guide the search. Typically, these techniques assume background knowledge in the form of explicit information about the desired clustering. In contrast, we consider the setting in which the background knowledge is instead about an undesired clustering. Such knowledge may be obtained from an existing classification or precedent algorithm. The problem is then to find a novel, "orthogonal" clustering in the data. We present a general algorithmic framework which makes use of cluster ensemble methods to solve this problem. One key advantage of this approach is that it takes a base clustering method which is used as a black box, allowing the practitioner to select the most appropriate clustering method for the domain. We present experimental results on synthetic and text data which establish the competitiveness of this framework.