Pattern Recognition
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mustererkennung 1998, 20. DAGM-Symposium
An EM Algorithm for the Block Mixture Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Reinforcement Learning Approach to Online Clustering
Neural Computation
Linked: How Everything Is Connected to Everything Else and What It Means
Linked: How Everything Is Connected to Everything Else and What It Means
A mixture model for random graphs
Statistics and Computing
Online EM algorithm for mixture with application to internet traffic modeling
Computational Statistics & Data Analysis
A new kernel-based algorithm for online clustering
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Learning graph prototypes for shape recognition
Computer Vision and Image Understanding
Improved Bayesian inference for the stochastic block model with application to large networks
Computational Statistics & Data Analysis
On the statistical detection of clusters in undirected networks
Computational Statistics & Data Analysis
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In the context of graph clustering, we consider the problem of simultaneously estimating both the partition of the graph nodes and the parameters of an underlying mixture of affiliation networks. In numerous applications the rapid increase of data size over time makes classical clustering algorithms too slow because of the high computational cost. In such situations online clustering algorithms are an efficient alternative to classical batch algorithms. We present an original online algorithm for graph clustering based on a Erdos-Renyi graph mixture. The relevance of the algorithm is illustrated, using both simulated and real data sets. The real data set is a network extracted from the French political blogosphere and presents an interesting community organization.