Learning non-redundant codebooks for classifying complex objects
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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
Detection of orthogonal concepts in subspaces of high dimensional data
Proceedings of the 18th ACM conference on Information and knowledge management
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning multiple nonredundant clusterings
ACM Transactions on Knowledge Discovery from Data (TKDD)
CoDA: interactive cluster based concept discovery
Proceedings of the VLDB Endowment
Model-based multidimensional clustering of categorical data
Artificial Intelligence
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
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
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
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Typical clustering algorithms output a single clustering of the data. However, in real world applications, data can often be interpreted in many different ways; data can have different groupings that are reasonable and interesting from different perspectives. This is especially true for high-dimensional data, where different feature subspaces may reveal different structures of the data. Why commit to one clustering solution while all these alternative clustering views might be interesting to the user. In this paper, we propose a new clustering paradigm for explorative data analysis: find all non-redundant clustering views of the data, where data points of one cluster can belong to different clusters in other views. We present a framework to solve this problem and suggest two approaches within this framework: (1) orthogonal clustering, and (2) clustering in orthogonal subspaces. In essence, both approaches find alternative ways to partition the data by projecting it to a space that is orthogonal to our current solution. The first approach seeks orthogonality in the cluster space, while the second approach seeks orthogonality in the feature space. We test our framework on both synthetic and high-dimensional benchmark data sets, and the results show that indeed our approaches were able to discover varied solutions that are interesting and meaningful. keywords: multi-view clustering, non-redundant clustering, orthogonalization