Analysis of Consensus Partition in Cluster Ensemble

Topchy, A.; Law, Max W. K.; Jain, Anil K.; Fred, Ana

doi:10.1109/icdm.2004.10100

Cited by 106 publications

(68 citation statements)

References 10 publications

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“…The votingbased literature utilizes different heuristics in attempting to solve the labelling correspondence problem. This problem is commonly formulated as a bipartite matching problem [6], where the optimal re-labelling is obtained by maximizing the agreement between the labelling of an ensemble partition with respect to a reference partition. The agreement is estimated by constructing a K × K contingency table between the two partitions, where K is the number of clusters in each partition 1 .…”

Section: Related Workmentioning

confidence: 99%

“…In the voting-based consensus function the label mismatch is defined as the problem of finding the optimal re-labelling of a given partition with respect to a reference partition. This problem is commonly formulated as a weighted bipartite matching formulation [6,7], and it is solved by inspecting whether data patterns in two partitions share labels more than with other clusters. In this paper, we present an alternative simple, yet robust, implementation for generating a consistent labelling scheme among the different partitions of the ensemble.…”

Section: Introductionmentioning

confidence: 99%

“…Two consensus functions are considered here, the Evidence Accumulation Clustering (EAC) [5] and the voting-based consensus function (e.g. [6,7]). …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering

Fouad

Randell

Galton

et al. 2017

Communications in Computer and Information Science

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Abstract. We present a framework for adapting consensus clustering methods with superpixels to segment oropharyngeal cancer images into tissue types (epithelium, stroma and background). The simple linear iterative clustering algorithm is initially used to split-up the image into binary superpixels which are then used as clustering elements. Colour features of the superpixels are extracted and fed into several base clustering approaches with various parameter initializations. Two consensus clustering formulations are then used, the Evidence Accumulation Clustering (EAC) and the voting-based function. They both combine the base clustering outcomes to obtain a single more robust consensus result. Unlike most unsupervised tissue image segmentation approaches that depend on individual clustering methods, the proposed approach allows for a robust detection of tissue compartments. For the voting-based consensus function, we introduce a technique based on image processing to generate a consistent labelling scheme among the base clustering outcomes. Experiments conducted on forty five hand-annotated images of oropharyngeal cancer tissue microarray cores show that the ensemble algorithm generates more accurate and stable results than individual clustering algorithms. The clustering performance of the voting-based consensus function using our re-labelling technique also outperforms the existing EAC.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering

Fouad

Randell

Galton

et al. 2017

Communications in Computer and Information Science

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“…Results of clustering using ETree in terms of %N M I. K is the number of experimental cluster centers. The next four columns are the performance of ETree itself together with using it as the base model of our boosting algorithm (CB) and applying voting [20], HGPA, and MCLA [5] consensus function learning methods. The next two columns indicate the results of using the methods of Frossyniotis et al [1] and Topchy et al [2] with ETree.…”

Section: Datasetsmentioning

confidence: 99%

“…We also implemented the algorithms proposed in [1,2] and compared their performance with our method. Additionally, we applied different consensus function learning methods, namely voting [20], HGPA, and MCLA [5], to the final ensemble. For all boosting methods we set the maximum number of base models to be 50 for ETree and 20 for k-means, respectively.…”

Section: Datasetsmentioning

confidence: 99%

Boosting for Model-Based Data Clustering

Saffari

Bischof

2008

Lecture Notes in Computer Science

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Abstract. In this paper a novel and generic approach for model-based data clustering in a boosting framework is presented. This method uses the forward stagewise additive modeling to learn the base clustering models. The experimental results on relatively large scale datasets and also Caltech4 object recognition set demonstrate how the performance of relatively simple and computationally efficient base clustering algorithms could be boosted using the proposed algorithm.

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Ensemble learning: A survey

Sagi

Rokach

2018

WIREs Data Min & Knowl

1,970

950

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Ensemble methods are considered the state-of-the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state-ofthe-art ensemble methods and discusses current challenges and trends in the field. This article is categorized under: Algorithmic Development > Ensemble Methods Technologies > Machine Learning Technologies > Classification K E Y W O R D S boosting, classifier combination, ensemble models, machine-learning, mixtures of experts, multiple classifier system, random forest 1 | INTRODUCTIONEnsemble learning is an umbrella term for methods that combine multiple inducers to make a decision, typically in supervised machine learning tasks. An inducer, also referred as a base-learner, is an algorithm that takes a set of labeled examples as input and produces a model (e.g., a classifier or regressor) that generalizes these examples. By using the produced model, predictions can be drawn for new unlabeled examples. An ensemble inducer can be of any type of machine learning algorithm (e.g., decision tree, neural network, linear regression model, etc.). The main premise of ensemble learning is that by combining multiple models, the errors of a single inducer will likely be compensated by other inducers, and as a result, the overall prediction performance of the ensemble would be better than that of a single inducer. Ensemble learning is usually regarded as the machine learning interpretation for the wisdom of the crowd. This concept can be illustrated through the story of Sir Francis Galton who was an English philosopher and statistician that conceived the basic concept of standard deviation and correlation. While visiting a livestock fair, Galton conducted a simple weight guessing contest. The participants were asked to guess the weight of an ox. Hundreds of people participated in this contest, but no one succeeded in guessing the weight: 1,198 pounds. Much to his surprise, Galton found that the average of all guesses came quite close to the exact weight: 1,198 pounds. In this experiment, Galton revealed the power of combining many predictions in order to obtain an accurate prediction. Ensemble methods manifest this concept in machine learning challenges, where they result in improved predictive performance compared to a single model. In addition, when the computational cost of the participating inducers is low (e.g., decision tree), ensemble models are often very efficient.

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Analysis of Consensus Partition in Cluster Ensemble

Cited by 106 publications

References 10 publications

Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering

Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering

Boosting for Model-Based Data Clustering

Ensemble learning: A survey

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