A clustering method based on boosting

Frossyniotis, Dimitrios S.; Likas, Aristidis; Stafylopatis, Andreas

doi:10.1016/j.patrec.2003.12.018

Cited by 67 publications

(47 citation statements)

References 4 publications

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“…The values are the average and standard deviation of NMI over 50 independent runs of the algorithms. 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.…”

Section: Datasetsmentioning

confidence: 99%

“…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. For those methods we only report their best performance over applying different consensus functions.…”

Section: Datasetsmentioning

confidence: 99%

“…Recently, there has been a few attempts to bring the idea of boosting to the clustering domain [1][2][3]. In general, ensemble clustering refers to producing data partitions by utilizing results delivered by a set of clustering models.…”

Section: Introductionmentioning

confidence: 99%

“…Frossyniotis et al [1] also used the concept of sub-sampling the dataset, by using two different performance measures for assessing the clustering quality for each sample, both based on the membership values of each sample to the individual clusters. They incorporated a very similar approach used in the original Discrete AdaBoost [11] for updating the weights for both samples and base models.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Boosting for Model-Based Data Clustering

Saffari

Bischof

2008

Lecture Notes in Computer Science

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“…Some authors have proposed to combine different clustering algorithms in a boosting-like framework. For instance [20] and [10] introduced weighted re-sampling of data points according to how reliably they are classified. In [16], a general iterative clustering algorithm is presented that combines several algorithms by keeping track both of point weights and of a membership coefficient for each pair of a point and a model.…”

Section: Introductionmentioning

confidence: 99%

CLOOSTING: CLustering Data with bOOSTING

Smeraldi

Bicego

Cristani

et al. 2011

Multiple Classifier Systems

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Abstract. We present a novel clustering approach, that exploits boosting as the primary means of modelling clusters. Typically, boosting is applied in a supervised classification context; here, we move in the less explored unsupervised scenario. Starting from an initial partition, clusters are iteratively re-estimated using the responses of one-vs-all boosted classifiers. Within-cluster homogeneity and separation between the clusters are obtained by a combination of three mechanisms: use of regularised Adaboost to reject outliers, use of weak learners inspired to subtractive clustering and smoothing of the decision functions with a Gaussian Kernel. Experiments on public datasets validate our proposal, in some cases improving on the state of the art.

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A general framework for efficient clustering of large datasets based on activity detection

Jin

Kim

Cao

et al. 2010

Statistical Analysis

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Data clustering is one of the most popular data mining techniques with broad applications. K-Means is one of the most popular clustering algorithms, due to its high efficiency/effectiveness and wide implementation in many commercial/noncommercial softwares. Performing efficient clustering on large dataset is especially useful; however, conducting K-Means clustering on large data suffers heavy computation burden which originates from the numerous distance calculations between the patterns and the centers. This paper proposes framework General Activity Detection (GAD) for fast clustering on large-scale data based on center activity detection. Within this framework, we design a set of algorithms for different scenarios: (i) exact GAD algorithm, E-GAD, which is much faster than K-Means and gets the same clustering result; (ii) approximate GAD algorithms with different assumptions, which are faster than E-GAD, while achieving different degrees of approximation; and (iii) GAD based algorithms to handle the large clusters problem which appears in many large-scale clustering applications. The framework provides a general solution to exploit activity detection for fast clustering in both exact and approximate scenarios, and our proposed algorithms within the framework can achieve very high speed. We have conducted extensive experiments on several datasets from various real world applications, including data compression, image clustering, and bioinformatics. By measuring the clustering quality and CPU time, the experiment results show the effectiveness and high efficiency of our proposed algorithms. 

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A clustering method based on boosting

Cited by 67 publications

References 4 publications

Boosting for Model-Based Data Clustering

Boosting for Model-Based Data Clustering

CLOOSTING: CLustering Data with bOOSTING

A general framework for efficient clustering of large datasets based on activity detection

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