Combining multiple weak clusterings

Topchy, A.; Jain, Anil K.; Punch, William F.

doi:10.1109/icdm.2003.1250937

Cited by 237 publications

(194 citation statements)

References 10 publications

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“…in which Y d is the data of pixels in one of the image class, X is the image label field, m d and s d are the mean and va− riance of the pixel value in the class, and y d is the pixel value in the class [11,12]. Based on conditional independent assumption of the whole data of the image Y, the conditional density P Y X ( )takes the form of…”

Section: Gaussian Mixture Model and Mrfmentioning

confidence: 99%

Mixture model and Markov random field-based remote sensing image unsupervised clustering method

Hou

Yang²,

Rao

et al. 2011

Opto-Electronics Review

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In this paper, a novel method for remote sensing image clustering based on mixture model and Markov random field (MRF) is proposed. A remote sensing image can be considered as Gaussian mixture model. The image clustering result corresponding to the image label field is a MRF. So, the image clustering procedure is transformed to a maximum a posterior (MAP) problem by Bayesian theorem. The intensity difference and the spatial distance between the two pixels in the same clique are introduced into the traditional MRF potential function. The iterative conditional model (ICM) is employed to find the solution of MAP. We use the max entropy criterion to choose the optimal clustering number. In the experiments, the method is compared with the traditional MRF clustering method using ICM and simulated annealing (SA). The results show that this method is better than the traditional MRF model both in noise filtering and miss-classification ratio.

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Section: Gaussian Mixture Model and Mrfmentioning

confidence: 99%

Mixture model and Markov random field-based remote sensing image unsupervised clustering method

Hou

Yang²,

Rao

et al. 2011

Opto-Electronics Review

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show abstract

“…The techniques presented in [10] compute a matrix of similarities between pairs of points, and then perform agglomerative clustering to generate a final clustering solution. In [20,21] the authors introduce new features to describe the data, and apply K-means and EM to output the final clustering solutions. Recently, several approaches have modeled the clustering ensemble problem as a graph partitioning problem [17,21].…”

Section: Cluster Ensemblesmentioning

confidence: 99%

Subspace Metric Ensembles for Semi-supervised Clustering of High Dimensional Data

Yan

Domeniconi

2006

Lecture Notes in Computer Science

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Abstract.A critical problem in clustering research is the definition of a proper metric to measure distances between points. Semi-supervised clustering uses the information provided by the user, usually defined in terms of constraints, to guide the search of clusters. Learning effective metrics using constraints in high dimensional spaces remains an open challenge. This is because the number of parameters to be estimated is quadratic in the number of dimensions, and we seldom have enough sideinformation to achieve accurate estimates. In this paper, we address the high dimensionality problem by learning an ensemble of subspace metrics. This is achieved by projecting the data and the constraints in multiple subspaces, and by learning positive semi-definite similarity matrices therein. This methodology allows leveraging the given side-information while solving lower dimensional problems. We demonstrate experimentally using high dimensional data (e.g., microarray data) the superior accuracy achieved by our method with respect to competitive approaches.

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“…Recently, there has been an emergent interest in studying cluster ensembles to enhance the quality and robustness of data clustering and to accommodate a wider variety of data types and clusters structures [3][4][5][6][7][8][9][10]. Some of the research have relied on using a co-association matrix as a voting medium for finding the combined partitioning.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Model Using Information Theoretic Measures for Cluster Ensembles

Ayad

Basir

Kamel

2004

Lecture Notes in Computer Science

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Abstract. This paper presents a probabilistic model for combining cluster ensembles utilizing information theoretic measures. Starting from a co-association matrix which summarizes the ensemble, we extract a set of association distributions, which are modelled as discrete probability distributions of the object labels, conditional on each data object. The key objectives are, first, to model the associations of neighboring data objects, and second, to allow for the manipulation of the defined probability distributions using statistical and information theoretic means. A Jensen-Shannon Divergence based Clustering Combination (JSDCC) method is proposed. The method selects cluster prototypes from the set of association distributions based on entropy maximization and maximization of the generalized JS divergence among the selected prototypes. The method proceeds by grouping association distributions by minimizing their JS divergences to the selected prototypes. By aggregating the grouped association distributions, we can represent empirical cluster conditional probability distributions of the object labels, for each of the combined clusters. Finally, data objects are assigned to their most likely clusters, and their cluster assignment probabilities are estimated. Experiments are performed to assess the presented method and compare its performance with other alternative co-association based methods.

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Combining multiple weak clusterings

Abstract: Abstract

Cited by 237 publications

References 10 publications

Mixture model and Markov random field-based remote sensing image unsupervised clustering method

Mixture model and Markov random field-based remote sensing image unsupervised clustering method

Subspace Metric Ensembles for Semi-supervised Clustering of High Dimensional Data

A Probabilistic Model Using Information Theoretic Measures for Cluster Ensembles

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