Matrix computations (3rd ed.)
Incremental clustering and dynamic information retrieval
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Extracting evolution of web communities from a series of web archives
Proceedings of the fourteenth ACM conference on Hypertext and hypermedia
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Dynamic social network analysis using latent space models
ACM SIGKDD Explorations Newsletter
Document clustering with prior knowledge
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Spectral Clustering, With Application To Speech Separation
The Journal of Machine Learning Research
Evolutionary spectral clustering by incorporating temporal smoothness
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
An event-based framework for characterizing the evolutionary behavior of interaction graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Indexing and querying XML using extended Dewey labeling scheme
Data & Knowledge Engineering
Quantifying sentiment and influence in blogspaces
Proceedings of the First Workshop on Social Media Analytics
Unsupervised video surveillance
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
EDA-USL: unsupervised clustering algorithm based on estimation of distribution algorithm
International Journal of Wireless and Mobile Computing
Social bookmark data mining using extended graph kernel
Proceedings of the Eleventh International Workshop on Multimedia Data Mining
A sparsity-inducing formulation for evolutionary co-clustering
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Evolutionary analysis of functional modules in dynamic PPI networks
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Maximum margin clustering on evolutionary data
Proceedings of the 21st ACM international conference on Information and knowledge management
A regularization framework in polar coordinates for transductive learning in networked data
Information Sciences: an International Journal
p-PIC: Parallel power iteration clustering for big data
Journal of Parallel and Distributed Computing
A regularized graph layout framework for dynamic network visualization
Data Mining and Knowledge Discovery
Dynamic joint sentiment-topic model
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Adaptive evolutionary clustering
Data Mining and Knowledge Discovery
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Evolutionary clustering is an emerging research area essential to important applications such as clustering dynamic Web and blog contents and clustering data streams. In evolutionary clustering, a good clustering result should fit the current data well, while simultaneously not deviate too dramatically from the recent history. To fulfill this dual purpose, a measure of temporal smoothness is integrated in the overall measure of clustering quality. In this article, we propose two frameworks that incorporate temporal smoothness in evolutionary spectral clustering. For both frameworks, we start with intuitions gained from the well-known k-means clustering problem, and then propose and solve corresponding cost functions for the evolutionary spectral clustering problems. Our solutions to the evolutionary spectral clustering problems provide more stable and consistent clustering results that are less sensitive to short-term noises while at the same time are adaptive to long-term cluster drifts. Furthermore, we demonstrate that our methods provide the optimal solutions to the relaxed versions of the corresponding evolutionary k-means clustering problems. Performance experiments over a number of real and synthetic data sets illustrate our evolutionary spectral clustering methods provide more robust clustering results that are not sensitive to noise and can adapt to data drifts.