Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Personalized information delivery: an analysis of information filtering methods
Communications of the ACM - Special issue on information filtering
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Latent semantic indexing: a probabilistic analysis
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Identifying Representative Trends in Massive Time Series Data Sets Using Sketches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Quantifiable data mining using ratio rules
The VLDB Journal — The International Journal on Very Large Data Bases
On clusterings-good, bad and spectral
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Higher-Order Web Link Analysis Using Multilinear Algebra
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Generalized Low Rank Approximations of Matrices
Machine Learning
Data streams: algorithms and applications
Foundations and Trends® in Theoretical Computer Science
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge and Information Systems
P2P authority analysis for social communities
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Two heads better than one: pattern discovery in time-evolving multi-aspect data
Data Mining and Knowledge Discovery
Colibri: fast mining of large static and dynamic graphs
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental tensor analysis: Theory and applications
ACM Transactions on Knowledge Discovery from Data (TKDD)
Sparse Super Symmetric Tensor Factorization
Neural Information Processing
Probabilistic Tensor Analysis with Akaike and Bayesian Information Criteria
Neural Information Processing
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
Hierarchical, Parameter-Free Community Discovery
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Robust foreground segmentation based on two effective background models
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Two-dimensional subspace classifiers for face recognition
Neurocomputing
Closed patterns meet n-ary relations
ACM Transactions on Knowledge Discovery from Data (TKDD)
Incremental pattern discovery on streams, graphs and tensors
ACM SIGKDD Explorations Newsletter
Learning patterns in the dynamics of biological networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social recommender systems for web 2.0 folksonomies
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Multi-way set enumeration in real-valued tensors
Proceedings of the 2nd Workshop on Data Mining using Matrices and Tensors
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
Community mining on dynamic weighted directed graphs
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
A New Incremental PCA Algorithm With Application to Visual Learning and Recognition
Neural Processing Letters
Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning
IEEE Transactions on Neural Networks
Tensor Framework and Combined Symmetry for Hypertext Mining
Fundamenta Informaticae
Fast computation of SimRank for static and dynamic information networks
Proceedings of the 13th International Conference on Extending Database Technology
Eigenvector-based clustering using aggregated similarity matrices
Proceedings of the 2010 ACM Symposium on Applied Computing
A unified tensor level set for image segmentation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Metric forensics: a multi-level approach for mining volatile graphs
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Parallel SimRank computation on large graphs with iterative aggregation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal recommendation on graphs via long- and short-term preference fusion
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards a matrix-based distributional model of meaning
HLT-SRWS '10 Proceedings of the NAACL HLT 2010 Student Research Workshop
Online evolutionary collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Adapting neighborhood and matrix factorization models for context aware recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Network growth and the spectral evolution model
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A novel split and merge technique for hypertext classification
Transactions on rough sets XII
Continuous summarization of co-evolving data in large water distribution network
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking
International Journal of Computer Vision
A survey of multilinear subspace learning for tensor data
Pattern Recognition
Social Network Analysis and Mining for Business Applications
ACM Transactions on Intelligent Systems and Technology (TIST)
Matrix-variate and higher-order probabilistic projections
Data Mining and Knowledge Discovery
PARAFAC algorithms for large-scale problems
Neurocomputing
Are tensor decomposition solutions unique? on the Global convergence HOSVD and parafac algorithms
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Common component analysis for multiple covariance matrices
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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
FAC'11 Proceedings of the 6th international conference on Foundations of augmented cognition: directing the future of adaptive systems
SCENT: Scalable compressed monitoring of evolving multirelational social networks
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Network node label acquisition and tracking
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Summarization Meets Visualization on Online Social Networks
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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
Empirical discriminative tensor analysis for crime forecasting
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Link prediction on evolving data using tensor factorization
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Factorizing YAGO: scalable machine learning for linked data
Proceedings of the 21st international conference on World Wide Web
Visual tracking via dynamic tensor analysis with mean update
Neurocomputing
Evolving social data mining and affective analysis methodologies, framework and applications
Proceedings of the 16th International Database Engineering & Applications Sysmposium
ciForager: Incrementally discovering regions of correlated change in evolving graphs
ACM Transactions on Knowledge Discovery from Data (TKDD)
Tensor Framework and Combined Symmetry for Hypertext Mining
Fundamenta Informaticae
Dynamic pagerank using evolving teleportation
WAW'12 Proceedings of the 9th international conference on Algorithms and Models for the Web Graph
Utilizing common substructures to speedup tensor factorization for mining dynamic graphs
Proceedings of the 21st ACM international conference on Information and knowledge management
ParCube: sparse parallelizable tensor decompositions
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Centrality and mode detection in dynamic contact graphs; a joint diagonalisation approach
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Discovery of extreme events-related communities in contrasting groups of physical system networks
Data Mining and Knowledge Discovery
Summarizing dynamic Social Tagging Systems
Expert Systems with Applications: An International Journal
Mining Deviations from Patient Care Pathways via Electronic Medical Record System Audits
ACM Transactions on Management Information Systems (TMIS) - Special Issue on Informatics for Smart Health and Wellbeing
Mining most frequently changing component in evolving graphs
World Wide Web
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How do we find patterns in author-keyword associations, evolving over time? Or in Data Cubes, with product-branch-customer sales information? Matrix decompositions, like principal component analysis (PCA) and variants, are invaluable tools for mining, dimensionality reduction, feature selection, rule identification in numerous settings like streaming data, text, graphs, social networks and many more. However, they have only two orders, like author and keyword, in the above example.We propose to envision such higher order data as tensors,and tap the vast literature on the topic. However, these methods do not necessarily scale up, let alone operate on semi-infinite streams. Thus, we introduce the dynamic tensor analysis (DTA) method, and its variants. DTA provides a compact summary for high-order and high-dimensional data, and it also reveals the hidden correlations. Algorithmically, we designed DTA very carefully so that it is (a) scalable, (b) space efficient (it does not need to store the past) and (c) fully automatic with no need for user defined parameters. Moreover, we propose STA, a streaming tensor analysis method, which provides a fast, streaming approximation to DTA.We implemented all our methods, and applied them in two real settings, namely, anomaly detection and multi-way latent semantic indexing. We used two real, large datasets, one on network flow data (100GB over 1 month) and one from DBLP (200MB over 25 years). Our experiments show that our methods are fast, accurate and that they find interesting patterns and outliers on the real datasets.