Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
The complexity of non-hierarchical clustering with instance and cluster level constraints
Data Mining and Knowledge Discovery
Intractability and clustering with constraints
Proceedings of the 24th international conference on Machine learning
Efficient incremental constrained clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Spectral clustering with inconsistent advice
Proceedings of the 25th international conference on Machine learning
Non-redundant Multi-view Clustering via Orthogonalization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Finding Alternative Clusterings Using Constraints
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Detection of orthogonal concepts in subspaces of high dimensional data
Proceedings of the 18th ACM conference on Information and knowledge management
Unifying dependent clustering and disparate clustering for non-homogeneous data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Boosting Clustering by Active Constraint Selection
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Model-based multidimensional clustering of categorical data
Artificial Intelligence
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
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Multi-view clustering using mixture models in subspace projections
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Model-based clustering of high-dimensional data: Variable selection versus facet determination
International Journal of Approximate Reasoning
Regularized nonnegative shared subspace learning
Data Mining and Knowledge Discovery
Guided learning for role discovery (GLRD): framework, algorithms, and applications
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Stochastic subspace search for top-k multi-view clustering
Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering
How to "alternatize" a clustering algorithm
Data Mining and Knowledge Discovery
Generating multiple alternative clusterings via globally optimal subspaces
Data Mining and Knowledge Discovery
Ensembles for unsupervised outlier detection: challenges and research questions a position paper
ACM SIGKDD Explorations Newsletter
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The aim of data mining is to find novel and actionable insights in data. However, most algorithms typically just find a single (possibly non-novel/actionable) interpretation of the data even though alternatives could exist. The problem of finding an alternative to a given original clustering has received little attention in the literature. Current techniques (including our previous work) are unfocused/unrefined in that they broadly attempt to find an alternative clustering but do not specify which properties of the original clustering should or should not be retained. In this work, we explore a principled and flexible framework in order to find alternative clusterings of the data. The approach is principled since it poses a constrained optimization problem, so its exact behavior is understood. It is flexible since the user can formally specify positive and negative feedback based on the existing clustering, which ranges from which clusters to keep (or not) to making a trade-off between alternativeness and clustering quality.