Two heads better than one: pattern discovery in time-evolving multi-aspect data
Data Mining and Knowledge Discovery
Incremental tensor analysis: Theory and applications
ACM Transactions on Knowledge Discovery from Data (TKDD)
Two Heads Better Than One: Pattern Discovery in Time-Evolving Multi-aspect Data
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Robust foreground segmentation based on two effective background models
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking
International Journal of Computer Vision
Dynamical Tensor Approximation
SIAM Journal on Matrix Analysis and Applications
Hybrid clustering of multiple information sources via HOSVD
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
GigaTensor: scaling tensor analysis up by 100 times - algorithms and discoveries
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Utilizing common substructures to speedup tensor factorization for mining dynamic graphs
Proceedings of the 21st ACM international conference on Information and knowledge management
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Data stream values are often associated with multiple aspects. For example, each value from environmental sensors may have an associated type (e.g., temperature, humidity, etc) as well as location. Aside from timestamp, type and location are the two additional aspects. How to model such streams? How to simultaneously find patterns within and across the multiple aspects? How to do it incrementally in a streaming fashion? In this paper, all these problems are addressed through a general data model, tensor streams, and an effective algorithmic framework, window-based tensor analysis (WTA). Two variations of WTA, independent-window tensor analysis (IW) and moving-window tensor analysis (MW), are presented and evaluated extensively on real datasets. Finally, we illustrate one important application, Multi-Aspect Correlation Analysis (MACA), which uses WTA and we demonstrate its effectiveness on an environmental monitoring application.