Co-clustering documents and words using bipartite spectral graph partitioning

By Inderjit S. Dhillon. Knowledge Discovery and Data Mining, 2001

1,971 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a69a816893573aaba052d2614c97d4a7a056a3aa

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Citations in the corpus

Document clustering by concept factorization (2004, neutral): “Spectral clustering based bipartite normalized cut (BP) [3] [15] 10.”
Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification (2009, background): “semisupervised clustering [1] [2] [3] [4] [5] and co-clustering [6] [7] [8] [9].”
Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models (2008, background): “Dhillon [40] co-clustered words and documents by partitioning a bipartite spectral graph with words and documents as vertices.”
Cross-domain sentiment classification via spectral feature alignment (2010, background): “Though our goal is only to cluster domain-specific features, it has been proved that clustering two related sets of points simultaneously can often get better results than clustering one single set of points only [14].”
Orthogonal nonnegative matrix t-factorizations for clustering (2006, methodology): “Simultaneously rows and columns clustering using Laplaci an matrix has been studied in [7, 35].”
A Unified Framework for Model-based Clustering (2003, methodology): “Despite this disadvantage, discriminative methods such as graph partitioning and spectral clustering algorithms (Karypis et al., 1999; Dhillon, 2001; Meila and Shi, 2001; Ng et al., 2002; Strehl and Ghosh, 2002) have gained recent popularity due to their ability to produce desirable clustering resul”
Classification in Networked Data: A Toolkit and a Univariate Case Study (2007, neutral): “This seeming pathology stems from the basic objective: the minimum of the sum of cut-through edge weights depends directly on the sizes of the cut sets; normalizing for the cut size leads to ratiocut optimization (Dhillon, 2001) constrained by the known labels (Joachims, 2003).”
Advances in Fuzzy Clustering and its Applications (2007, methodology): “This method has also been previously used to simultaneously cluster words and documents by Dhillon (2001) .”
Detecting unusual activity in video (2004, methodology): “This concept has been explored in the document–keyword analysis by Dhillon [4] and in bioinformatics by Kluger et al.”
A causal framework for explaining the predictions of black-box sequence-to-sequence models (2017, methodology): “For the partitioning step we compare the robust formulation described above with two classical approaches to bipartite graph partitioning which do not take uncertainty into account: the coclustering method of Dhillon (2001) and the biclustering method of Kluger et al. (2003).”
A survey of kernel and spectral methods for clustering (2008, methodology): “Spectral methods have been applied in clustering of artificial data [35,36], in image segmentation [23,37,38], in bioinformatics [39], and in co-clustering problems of words and documents [40] and genes and conditions [41].”
Transductive Learning via Spectral Graph Partitioning (2003, background): “Following (Dhillon, 2001) and ignoring the constraints, the unsupervised ratiocut optimization problem can equivalently be written as”
Unsupervised Learning (2018, neutral): “The goal of co-clustering is to generate biclusters/co-clusters: a subset of rows that exhibit similar behavior across a subset of columns (11).”
Understanding Hidden Memories of Recurrent Neural Networks (2017, methodology): “In the prototype of RNN evaluator, the spectral co-clustering algorithm [8] with kmeans++ initialization is used to simultaneously cluster hidden state units and words.”
Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering (2004, methodology): “Viewing the documents in this fashion, a number of edge-cut-based criterion functions can be used to cluster document datasets (Cheng & Wei, 1991; Hagen & Kahng, 1991; Shi & Malik, 2000; Ding et al., 2001; Zha et al., 2001a; Dhillon, 2001).”
A Survey of Clustering Data Mining Techniques (2006, methodology): “An advanced algebraic approach to co-clustering based on bi-partite graphs and their minimal cuts in conjunction with text mining was developed in [57].”