Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Clustering and Dynamic Information Retrieval
SIAM Journal on Computing
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
Mining and Visualizing the Evolution of Subgroups in Social Networks
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A tutorial on spectral clustering
Statistics and Computing
Community evolution in dynamic multi-mode networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting and Tracking Spatio-temporal Clusters with Adaptive History Filtering
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Dirichlet Process Based Evolutionary Clustering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Evolutionary Clustering by Hierarchical Dirichlet Process with Hidden Markov State
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Analyzing communities and their evolutions in dynamic social networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Incremental spectral clustering by efficiently updating the eigen-system
Pattern Recognition
A kernel approach to comparing distributions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
On evolutionary spectral clustering
ACM Transactions on Knowledge Discovery from Data (TKDD)
On-line evolutionary exponential family mixture
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Flexible constrained spectral clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Unifying dependent clustering and disparate clustering for non-homogeneous data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Shrinkage algorithms for MMSE covariance estimation
IEEE Transactions on Signal Processing
Tracking the Evolution of Communities in Dynamic Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
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In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naïve estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.