Mining Quantitative Frequent Itemsets Using Adaptive Density-Based Subspace Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Clicks: An effective algorithm for mining subspace clusters in categorical datasets
Data & Knowledge Engineering
VISA: visual subspace clustering analysis
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Morpheus: interactive exploration of subspace clustering
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Pleiades: Subspace Clustering and Evaluation
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
EDSC: efficient density-based subspace clustering
Proceedings of the 17th ACM conference on Information and knowledge management
HSM: Heterogeneous Subspace Mining in High Dimensional Data
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Subspace and projected clustering: experimental evaluation and analysis
Knowledge and Information Systems
Evaluating clustering in subspace projections of high dimensional data
Proceedings of the VLDB Endowment
Projected Gustafson Kessel Clustering
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
DBSC: a dependency-based subspace clustering algorithm for high dimensional numerical datasets
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Mining quality-aware subspace clusters
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Pattern Recognition Letters
Advancing data clustering via projective clustering ensembles
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Projected Gustafson-Kessel clustering algorithm and its convergence
Transactions on rough sets XIV
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
A survey on enhanced subspace clustering
Data Mining and Knowledge Discovery
Projective clustering ensembles
Data Mining and Knowledge Discovery
Hi-index | 0.00 |
High-dimensional data pose challenges to traditional clustering algorithms due to their inherent sparsity and data tend to cluster in different and possibly overlapping subspaces of the entire feature space. Finding such subspaces is called subspace mining. We present SCHISM, a new algorithm for mining interesting subspaces, using the notions of support and Chernoff-Hoeffding bounds. We use a vertical representation of the dataset, and use a depth-first search with backtracking to find maximal interesting subspaces. We test our algorithm on a number of high-dimensional synthetic and real datasets to test its effectiveness.