Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning

Rege, Manjeet; Dong, Ming; Fotouhi, Farshad

doi:10.1109/icdm.2006.36

Cited by 52 publications

(32 citation statements)

References 17 publications

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“…Using an iterative approach, sub-matrices of the original matrix were derived. Another popular approach that has been taken for co-clustering is treating it as a problem of partitioning bipartite graphs [9]- [11]. However, in spite of the above efforts, co-clustering of evolving data, has remained unaddressed.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary spectral co-clustering

Green

Rege

Liu

et al. 2011

The 2011 International Joint Conference on Neural Networks

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Abstract-Co-clustering is the problem of deriving submatrices from the larger data matrix by simultaneously clustering rows and columns of the data matrix. Traditional co-clustering techniques are inapplicable to problems where the relationship between the instances (rows) and features (columns) evolve over time. Not only is it important for the clustering algorithm to adapt to the recent changes in the evolving data, but it also needs to take the historical relationship between the instances and features into consideration. We present ESCC, a general framework for evolutionary spectral co-clustering. We are able to efficiently co-cluster evolving data by incorporation of historical clustering results. Under the proposed framework, we present two approaches, Respect To the Current (RTC), and Respect To Historical (RTH). The two approaches differ in the way the historical cost is computed. In RTC, the present clustering quality is of most importance and historical cost is calculated with only one previous timestep. RTH, on the other hand, attempts to keep instances and features tied to the same clusters between time-steps. Extensive experiments performed on synthetic and real world data, demonstrate the effectiveness of the approach.

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Section: Introductionmentioning

confidence: 99%

Evolutionary spectral co-clustering

Green

Rege

Liu

et al. 2011

The 2011 International Joint Conference on Neural Networks

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“…Just as the columns of V provide a basis which can be used to discover document clusters, we can use the columns of U to discover a basis which correspond to word clusters. As we will see later, document clusters and word clusters are closely related, and it is often useful to discover both simultaneously, as in frameworks such as co-clustering [30,31,75]. Matrix-factorization provides a natural way of achieving this goal.…”

Section: Non-negative Matrix Factorizationmentioning

confidence: 99%

“…In [30], it has been shown how a spectral partitioning algorithm can be used effectively for this purpose. Another method discussed in [75] uses an isometric bipartite graph-partitioning approach for the clustering process.…”

Section: 31mentioning

confidence: 99%

A Survey of Text Clustering Algorithms

2012

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Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this chapter, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the text domain. We will discuss the key methods used for text clustering, and their relative advantages. We will also discuss a number of recent advances in the area in the context of social network and linked data.

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“…In these cases, feature subsets associated to individual clusters is more useful and can provide us a better understanding of the data. In bipartite graph partitioning [16], [17], features are grouped together with patterns in each cluster. However, features are divided exclusively, which prevents the possibility of a feature being relevant to more than one cluster.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous Localized Feature Selection and Model Detection for Gaussian Mixtures

Dong

Hua

2009

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-In this paper, we propose a novel approach of simultaneous localized feature selection and model detection for unsupervised learning. In our approach, local feature saliency, together with other parameters of Gaussian mixtures, are estimated by Bayesian variational learning. Experiments performed on both synthetic and real-world datasets illustrate that our approach is superior over both global feature selection and subspace clustering methods.

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Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning

Cited by 52 publications

References 17 publications

Evolutionary spectral co-clustering

Evolutionary spectral co-clustering

A Survey of Text Clustering Algorithms

Simultaneous Localized Feature Selection and Model Detection for Gaussian Mixtures

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