HSM: Heterogeneous Subspace Mining in High Dimensional Data
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
EDISKCO: energy efficient distributed in-sensor-network k-center clustering with outliers
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Detection of orthogonal concepts in subspaces of high dimensional data
Proceedings of the 18th ACM conference on Information and knowledge management
Evaluating clustering in subspace projections of high dimensional data
Proceedings of the VLDB Endowment
Swarm: mining relaxed temporal moving object clusters
Proceedings of the VLDB Endowment
An extension of the PMML standard to subspace clustering models
Proceedings of the 2011 workshop on Predictive markup language modeling
Efficient selectivity estimation by histogram construction based on subspace clustering
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Scalable density-based subspace clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
External evaluation measures for subspace clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Short communication: Algorithm to determine ε-distance parameter in density based clustering
Expert Systems with Applications: An International Journal
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Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of projections is exponential in the number of dimensions, efficiency is crucial. Moreover, the resulting subspace clusters are often highly redundant, i.e. many clusters are detected multiply in several projections. We propose a novel index for efficient subspace clustering in a novel depth-first processing with in-process-removal of redundant clusters for better pruning. Thorough experiments on real and synthetic data show that INSCY yields substantial efficiency and quality improvements.