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On the Best Rank-1 and Rank-(R1,R2,. . .,RN) Approximation of Higher-Order Tensors
SIAM Journal on Matrix Analysis and Applications
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Rank-One Approximation to High Order Tensors
SIAM Journal on Matrix Analysis and Applications
On the Best Rank-1 Approximation of Higher-Order Supersymmetric Tensors
SIAM Journal on Matrix Analysis and Applications
Orthogonal Tensor Decompositions
SIAM Journal on Matrix Analysis and Applications
SIAM Journal on Matrix Analysis and Applications
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IV '03 Proceedings of the Seventh International Conference on Information Visualization
Locating hidden groups in communication networks using hidden Markov models
ISI'03 Proceedings of the 1st NSF/NIJ conference on Intelligence and security informatics
Higher-Order Web Link Analysis Using Multilinear Algebra
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Two heads better than one: pattern discovery in time-evolving multi-aspect data
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DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
Computational Linguistics
Effective hybrid recommendation combining users-searches correlations using tensors
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
MultiRank: co-ranking for objects and relations in multi-relational data
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Collective sampling and analysis of high order tensors for chatroom communications
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Venn diagram construction of internet chatroom conversations
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Tripartite community structure in social bookmarking data
The New Review of Hypermedia and Multimedia - Special issue on Social Linking and Hypermedia
Tensor clustering via adaptive subspace iteration
Intelligent Data Analysis
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This work identifies the limitations of n-way data analysis techniques in multidimensional stream data, such as Internet chatroom communications data, and establishes a link between data collection and performance of these techniques. Its contributions are twofold. First, it extends data analysis to multiple dimensions by constructing n-way data arrays known as high order tensors. Chatroom tensors are generated by a simulator which collects and models actual communication data. The accuracy of the model is determined by the Kolmogorov-Smirnov goodness-of-fit test which compares the simulation data with the observed (real) data. Second, a detailed computational comparison is performed to test several data analysis techniques including svd [1], and multiway techniques including Tucker1, Tucker3 [2], and Parafac [3].