Cooperative clustering

Kashef, Rasha; Kamel, Mohamed S.

doi:10.1016/j.patcog.2009.12.018

Cited by 62 publications

(35 citation statements)

References 31 publications

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“…A similar approach has been proposed by [21]. The authors defined a new cooperative clustering model which involves cooperation among multiple clustering techniques to increase the homogeneity of objects within the clusters.…”

Section: Collaboration Of Existing Clustering Methodsmentioning

confidence: 99%

“…[21] presented a cooperative clustering model mainly based on four compo-nents (1) Co-occurred sub-clusters, (2) An histogram representation of the pair-wise similarities within sub-clusters, (3) The cooperative contingency graph, and (4) The coherent merging between the set of histograms. This kind of Cooperative Clustering model is based on finding the intersection between the multiple clusterings in terms of a set of sub-clusters.…”

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confidence: 99%

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Collaborative clustering: Why, when, what and how

et al. 2018

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Highlights• A didactic presentation of issues raised by clustering is given.• An up-to-date review and classi cation of collaborative clustering methods is presented.• The questions about why, when and how collaborative clustering can help are addressed. AbstractClustering is one type of unsupervised learning where the goal is to partition the set of objects into groups called clusters. Faced to the difficulty to design a general purpose clustering algorithm and to choose a good, let alone perfect, set of criteria for clustering a data set, one solution is to resort to a variety of clustering procedures based on different techniques, parameters and/or initializations, in order to construct one (or several) final clustering(s). The hope is that by combining several clustering solutions, each one with its own bias and imperfections, one will get a better overall solution.In the cooperative clustering model, as Ensemble Clustering, a set of clustering algorithms are used in parallel on a given data set: the local results are combined to get an hopefully better overall clustering. In the collaborative framework, the goal is that each local computation, quite possibly applied to distinct data sets, benefit from the work done by the other collaborators. This paper is dedicated to collaborative clustering. In particular, after a brief overview of clustering and the major issues linked to, it presents main challenges related to organize and control the collaborative process.

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Section: Collaboration Of Existing Clustering Methodsmentioning

confidence: 99%

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confidence: 99%

Collaborative clustering: Why, when, what and how

et al. 2018

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“…Ensemble approach is well known as a prominent method for improving robustness, stability and accuracy of clustering solutions [6][7][8][9][10][11][12][13][14][15]. Ensemble approach combines multiple partitions generated by clustering algorithms into a single consensual solution.…”

Section: Introductionmentioning

confidence: 99%

“…Research on clustering ensembles focuses on two challenging aspects: how to generate different and diverse partitions and how to design consensus function. There are four common ways of generating multiple data partitions for clustering ensemble [6]: applying different clustering algorithms [7], applying the same clustering algorithm with different values of parameters or initializations [8], combining data representations (feature spaces) [9] and different subsets of initial dataset [10].…”

Section: Introductionmentioning

confidence: 99%

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Computationally efficient methods of clustering ensemble construction for satellite image segmentation

Пестунов¹,

Rylov²,

Sinyavskiy³

et al. 2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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Combining multiple partitions into single ensemble clustering solution is a prominent way to improve accuracy and stability of clustering solutions. One of the major problems in constructing clustering ensembles is high computational complexity of the common methods. In this paper two computationally efficient methods of constructing ensembles of nonparametric clustering algorithms are introduced. They are based on the use of co-association matrix and subclusters. The results of experiments on synthetic and real datasets confirm their effectiveness and show the stability of the obtained solutions. The performance of the proposed methods allows to process large images including multispectral satellite data.

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Recommendations Based on Collective Intelligence – Case of Customer Segmentation

Pondel

Korczak

2019

Lecture Notes in Business Information Processing

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Cooperative clustering

Cited by 62 publications

References 31 publications

Collaborative clustering: Why, when, what and how

Collaborative clustering: Why, when, what and how

Computationally efficient methods of clustering ensemble construction for satellite image segmentation

Recommendations Based on Collective Intelligence – Case of Customer Segmentation

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