Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Clustering through decision tree construction
Proceedings of the ninth international conference on Information and knowledge management
A new cell-based clustering method for large, high-dimensional data in data mining applications
Proceedings of the 2002 ACM symposium on Applied computing
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
d-Clusters: Capturing Subspace Correlation in a Large Data Set
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
MaPle: A Fast Algorithm for Maximal Pattern-based Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Dual Clustering: Integrating Data Clustering over Optimization and Constraint Domains
IEEE Transactions on Knowledge and Data Engineering
On mining cross-graph quasi-cliques
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Multi-view clustering via canonical correlation analysis
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Active learning with multiple views
Journal of Artificial Intelligence Research
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
m-SNE: multiview stochastic neighbor embedding
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Locality mutual clustering for document retrieval
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
Quality of information-based source assessment and selection
Neurocomputing
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In many applications, such as bioinformatics and cross-market customer relationship management, there are data from multiple sources jointly describing the same set of objects. An important data mining task is to find interesting groups of objects that form clusters in subspaces of the data sources jointly supported by those data sources. In this paper, we study a novel problem of mining mutual subspace clusters from multiple sources. We develop two interesting models and the corresponding methods for mutual subspace clustering. The density-based model identifies dense regions in subspaces as clusters. The bottom-up method searches for density-based mutual subspace clusters systematically from low-dimensional subspaces to high-dimensional ones. The partitioning model divides points in a data set into k exclusive clusters and a signature subspace is found for each cluster, where k is the number of clusters desired by a user. The top-down method interleaves the well-known k-means clustering procedures in multiple sources. We use experimental results on synthetic data sets and real data sets to report the effectiveness and the efficiency of the methods.