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Sparse Poisson Latent Block Model for Document Clustering

https://doi.org/10.1109/tkde.2017.2681669
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Co‐Clustering
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Two-mode multi-partitioning
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Block clustering with Bernoulli mixture models: Comparison of different approaches
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Orthogonal nonnegative matrix tri-factorization for co-clustering: Multiplicative updates on Stiefel manifolds
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Clustering with block mixture models
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Concept Decompositions for Large Sparse Text Data Using Clustering
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Objective Criteria for the Evaluation of Clustering Methods
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Direct Clustering of a Data Matrix
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Co-clustering for Binary and Categorical Data with Maximum Modularity
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Matplotlib: A 2D Graphics Environment
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Biclustering algorithms for biological data analysis: a survey
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Frequency-Sensitive Competitive Learning for Scalable Balanced Clustering on High-Dimensional Hyperspheres
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An EM algorithm for the block mixture model
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Exemplar-based Visualization of Large Document Corpus (InfoVis2009-1115)
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Maximum Likelihood from Incomplete Data Via the EM Algorithm
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Orthogonal nonnegative matrix t-factorizations for clustering
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SCOAL
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Co-clustering Document-term Matrices by Direct Maximization of Graph Modularity
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Co-clustering documents and words using bipartite spectral graph partitioning
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Information-theoretic co-clustering
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Two-mode clustering methods: astructuredoverview
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CLUTO - A Clustering Toolkit
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Clustering Criteria and Multivariate Normal Mixtures
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HANDLING THE IMPACT OF LOW FREQUENCY EVENTS ON CO-OCCURRENCE BASED MEASURES OF WORD SIMILARITY - A Case Study of Pointwise Mutual Information

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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