A principled and flexible framework for finding alternative clusterings
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Avoiding Bias in Text Clustering Using Constrained K-means and May-Not-Links
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Which clustering do you want? inducing your ideal clustering with minimal feedback
Journal of Artificial Intelligence Research
An experimental study of constrained clustering effectiveness in presence of erroneous constraints
Information Processing and Management: an International Journal
Wiley Interdisciplinary Reviews: 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
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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The aim of data mining is to find novel and actionable insights. However, most algorithms typically just find a single explanation of the data even though alternatives could exist. In this work, we explore a general purpose approach to find an alternative clustering of the data with the aid of must-link and cannot-link constraints. This problem has received little attention in the literature and since our approach can be incorporated into the many clustering algorithms that use a distance function, compares favorably with existing work.