Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
P3C: A Robust Projected Clustering Algorithm
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Non-redundant Multi-view Clustering via Orthogonalization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
DUSC: Dimensionality Unbiased Subspace Clustering
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
INSCY: Indexing Subspace Clusters with In-Process-Removal of Redundancy
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A principled and flexible framework for finding alternative clusterings
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
CoDA: interactive cluster based concept discovery
Proceedings of the VLDB Endowment
Proceedings of the 14th International Conference on Extending Database Technology
An extension of the PMML standard to subspace clustering models
Proceedings of the 2011 workshop on Predictive markup language modeling
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
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Substructure clustering: a novel mining paradigm for arbitrary data types
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
A survey on enhanced subspace clustering
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
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In the knowledge discovery process, clustering is an established technique for grouping objects based on mutual similarity. However, in today's applications for each object very many attributes are provided. As multiple concepts described by different attributes are mixed in the same data set, clusters do not appear in all dimensions. In these high dimensional data spaces, each object can be clustered in several projections of the data. However, recent clustering techniques do not succeed in detection of these orthogonal concepts hidden in the data. They either miss multiple concepts for each object by partitioning approaches or provide redundant clusters in very similar subspaces. In this work we propose a novel clustering method aiming only at orthogonal concept detection in subspaces of the data. Unlike existing clustering approaches, OSCLU (Orthogonal Subspace CLUstering) detects for each object the orthogonal concepts described by differing attributes while pruning similar concepts. Thus, each detected cluster in an orthogonal subspace provides novel information about the hidden structure of the data. Thorough experiments on real and synthetic data show that OSCLU yields substantial quality improvements over existing clustering approaches.