Matrix computations (3rd ed.)
ACM Computing Surveys (CSUR)
Rank-One Approximation to High Order Tensors
SIAM Journal on Matrix Analysis and Applications
Orthogonal Tensor Decompositions
SIAM Journal on Matrix Analysis and Applications
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Adaptive dimension reduction for clustering high dimensional data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Document clustering via adaptive subspace iteration
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
CubeSVD: a novel approach to personalized Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Rank-R Approximation of Tensors: Using Image-as-Matrix Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ParTriCluster: A Scalable Parallel Algorithm for Gene Expression Analysis
SBAC-PAD '06 Proceedings of the 18th International Symposium on Computer Architecture and High Performance Computing
Simultaneous Component and Clustering Models for Three-way Data: Within and Between Approaches
Journal of Classification
Adaptive dimension reduction using discriminant analysis and K-means clustering
Proceedings of the 24th international conference on Machine learning
Algorithms for clustering high dimensional and distributed data
Intelligent Data Analysis
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal Analysis of Semantic Graphs Using ASALSAN
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Clustering multi-way data via adaptive subspace iteration
Proceedings of the 17th ACM conference on Information and knowledge management
Experiments with random projection
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Modeling and multiway analysis of chatroom tensors
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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Multi-way data or tensors are generalizations of matrices. Clustering multi-way data is a very important research topic due to the intrinsic rich structures in real-world datasets. Despite significant progress made on subspace clustering for two-way data, few attempts have been made to develop subspace clustering algorithms on multi-way data. In this paper, we propose the subspace clustering algorithm on multi-way data, called ASI-T Adaptive Subspace Iteration on Tensor. We show that ASI-T is a special version of High Order SVD HOSVD, a commonly used tensor factorization method. We show that ASI-T is simultaneously performing subspace identification using 2DSVD identifying the subspace structure of the tensor from the current data clusters and data clustering using K-Means clustering the data units on the current identified subspaces. By explicitly modeling subspace structures, ASI-T is also able to generate interpretable clustering results. The experimental results on both synthetic data and real-world data demonstrate the effectiveness of ASI-T.