Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Non-redundant clustering
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Non-redundant Multi-view Clustering via Orthogonalization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Statistical Analysis and Data Mining
Finding Alternative Clusterings Using Constraints
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Multiobjective data clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An architecture for component-based design of representative-based clustering algorithms
Data & Knowledge Engineering
A novel approach for finding alternative clusterings using feature selection
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Multi-view clustering using mixture models in subspace projections
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
How to "alternatize" a clustering algorithm
Data Mining and Knowledge Discovery
Generating multiple alternative clusterings via globally optimal subspaces
Data Mining and Knowledge Discovery
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Discovery of alternative clusterings is an important method for exploring complex datasets. It provides the capability for the user to view clustering behaviour from different perspectives and thus explore new hypotheses. However, current algorithms for alternative clustering have focused mainly on linear scenarios and may not perform as desired for datasets containing clusters with non linear shapes. Our goal in this paper is to address this challenge of non linearity. In particular, we propose a novel algorithm to uncover an alternative clustering that is distinctively different from an existing, reference clustering. Our technique is information theory based and aims to ensure alternative clustering quality by maximizing the mutual information between clustering labels and data observations, whilst at the same time ensuring alternative clustering distinctiveness by minimizing the information sharing between the two clusterings. We perform experiments to assess our method against a large range of alternative clustering algorithms in the literature. We show our technique's performance is generally better for non-linear scenarios and furthermore, is highly competitive even for simpler, linear scenarios.