Information-theoretic co-clustering

By Inderjit S. Dhillon, Subramanyam Mallela, Dharmendra S. Modha. 2003

1,298 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Information Retrieval & Search, Probabilistic & Bayesian Methods, Signal Processing

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Citations in the corpus

Invariant Information Clustering for Unsupervised Image Classification and Segmentation (2018, background): “More generally, learning from paired data has been explored in co-clustering [25, 16] and in other works [50] that build on the information bottleneck principle [20].”
A Comprehensive Survey on Transfer Learning (2019, methodology): “For example, some transfer learning approaches implicitly reduce the features by using the co-clustering technique, that is, simultaneously clustering both the columns and rows of (or say, co-cluster) a contingency table based on the information theory [85].”
Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data (2004, background): “[8] propose an information-theoretic coclustering algorithm that views a non-negative matrix as an empirical joint probability distribution of two discrete random variables and poses the co-clustering problem as an optimization problem in information theory: the optimal co-clustering maximizes the m”
V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure (2007, methodology): “…techniques (misclassiﬁcation index (MI) (Zeng et al., 2002), H (Meila and Heckerman, 2001), L (Larsen and Aone, 1999), D (van Dongen, 2000), micro-averaged precision and recall (Dhillon et al., 2003)), F-measure relies on a post-processing step in which each cluster is assigned to a class.”
PAC-Bayesian Analysis of Co-clustering and Beyond (2010, methodology): “This trade-off suggests a modification to the original objective of co-clustering in Dhillon et al. (2003), which is maximization ofI(C1;C2) alone (Dhillon et al., 2003 discuss the case of two-dimensional matrices).”
Multi-View Learning in the Presence of View Disagreement (2008, methodology): “Our work bears similarity to co-clustering approaches which use co-occurrence statistics to perform multi-view clustering [10, 9, 8].”
Local Modeling of Attributed Graphs: Algorithms and Applications (2016, methodology): “Autopart, cross-associations [26], and information-theoretic co-clustering [38] are parameter-free examples to graph clustering methods.”
Graph based anomaly detection and description: a survey (2014, background): “…2004],[Newman, 2006]; co-clustering for concurrent clustering of the rows and the columns of the adjacency matrix of a graph [Chakrabarti, 2004, Dhillon et al., 2003], and its distributed variants [Papadimitriou and Sun, 2008]; dynamic community detection algorithms Tantipathananandh and…”
Fast Random Walk with Restart and Its Applications (2006, methodology): “METIS [19], spectral clustering [20], flow simulation [8], co -clustering [ 6 ], and the betweenness based method [11].”
A scalable collaborative filtering framework based on co-clustering (2005, background): “There has been a lot of work on co-clustering [8, 7], most of which was focused on handling sparsity and dimensionality reduction issues in text and micro-array analysis.”
Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation (2012, background): “Minimizing the entropy (or similarly, maximizing the mutual information) has been previously studied in the context of (discriminative) clustering, cf. (Gomes et al., 2010; Dhillon et al., 2003).”
Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering (2016, background): “ formal relation of our approach to matrix factorization techniques in Section4.5. Co-clustering:The majority of the literature on co-clustering considers the detection of non-overlapping co-clusters [10], [29]. Notable exceptions are the approach in [3], which introduces a generative model for over”
Fast discovery of connection subgraphs (2004, neutral): “Other related work includes the PageRank [21] and personalized PageRank algorithms [15, 16]; graph clustering, partioning, and matrix reordering [2, 4, 17, 23, 24]; electrical circuits and random walks [3, 7, 22]; and influence propagation [18].”