A multiple clustering combination approach based on iterative voting process

Khedairia, Soufiane; Khadir, Mohamed Tarek

doi:10.1016/j.jksuci.2019.09.013

Cited by 21 publications

(17 citation statements)

References 25 publications

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“…There are ongoing efforts to address the computational complexity, such as improvements to SC3 [30]. Other solutions to the computational complexity may come from advances in the statistical and computational literature, such as consensus formed on heuristics of cluster similarity using metrics such as the Jaccard index [31]. In the meantime, clustering on geometric medians is likely to be a viable solution for cytometry data analysis.…”

Section: Discussionmentioning

confidence: 99%

GeoWaVe: Geometric median clustering with weighted voting for ensemble clustering of cytometry data

Burton

Cuff

Kift-Morgan

et al. 2022

Preprint

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Motivation: Clustering is an unsupervised method for identifying structure in unlabelled data. In the context of cytometry, is typically used to categorise cells into subpopulations of similar phenotype. However, clustering is greatly dependent on hyperparameters and the data to which it is applied as each algorithm makes different assumptions and generates a different 'view' of the dataset. As such, the choice of clustering algorithm can significantly influence results, and there is often not one preferred method but different insights to be obtained from different methods. To overcome these limitations, consensus approaches are needed that directly address the effect of competing algorithms, which to our knowledge has not been applied to cytometry. Results: We here present a novel ensemble clustering methodology based on geometric median clustering with weighted voting (GeoWaVe). Compared to graph ensemble clustering methods that have gained popularity in scRNA-seq analysis, GeoWaVe performed favourably on different sets of high-dimensional mass and flow cytometry data. Our findings provide proof of concept for the power of consensus methods to make cytometry data analysis more robust and reproducible. The availability of ensemble clustering methods has the potential to represent a sea change in big data analysis, with implications for an improved understanding of cellular responses, clinical conditions, and therapeutic and diagnostic options. Availability and implementation: GeoWaVe is available as part of the CytoCluster package https://github.com/burtonrj/CytoCluster.

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Section: Discussionmentioning

confidence: 99%

GeoWaVe: Geometric median clustering with weighted voting for ensemble clustering of cytometry data

Burton

Cuff

Kift-Morgan

et al. 2022

Preprint

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“…Several the relabeling are based methods Plurality Voting (PV) [26], Voting‐Merging (V‐M) [27], Voting for fuzzy clustering [28], voting Active Clusters (VAC) [29], Soft‐Voting Clustering Ensemble (SVCE) [30] and many others [31, 32]. Our approach is based on the relabeling which will be described in Section 3.3.…”

Section: The Clustering Ensemble Methodsmentioning

confidence: 99%

Multiple classifier system for remotely sensed data clustering

Ramdane

Mahi

Daho

et al. 2021

IET Image Processing

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The Multiple Classifier System (or classifier ensemble) is the consensus of different clustering algorithms that can provide high accuracy for the best partition and thus overcome the constraints of conventional approaches based on single classifiers. The MCS is divided into two stages: Partition creation and partition combining. The potential benefits of this methodology in unsupervised land cover categorization utilizing synthetic, composite, and remotely sensed data are investigated in this paper. Four clustering algorithms are used for the MCS's first step, and according to the WB index, the best‐unsupervised classification is obtained. In the second stage, relabeling and, voting approaches are then applied. The MCS's experimental results outperform the individual clustering outcomes in terms of accuracy.

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“…One such technique is cooperative clustering that combines different clustering approaches. Ensemble/cooperative methods provide more accurate and robust solutions in comparison with individual techniques [33]. Cooperative clustering has largely been explored in various domains including software modularization [34], [35] and pattern recognition [36], text classification [37].…”

Section: Have Explored Twitter Datamentioning

confidence: 99%

“…They performed experimental evaluation on five open source software systems and found that the proposed cooperative-clustering framework offers better performance. In [33], clustering ensemble is examined such that multiple clustering techniques are combined for a robust and stable solution. For this purpose an Iterative Combining Clusterings Method (ICCM) is proposed.…”

Section: Related Workmentioning

confidence: 99%

A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis

et al. 2020

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Twitter sentiment analysis is a challenging problem in natural language processing. For this purpose, supervised learning techniques have mostly been employed, which require labeled data for training. However, it is very time consuming to label datasets of large size. To address this issue, unsupervised learning techniques such as clustering can be used. In this study, we explore the possibility of using hierarchical clustering for twitter sentiment analysis. Three hierarchical-clustering techniques, namely single linkage (SL), complete linkage (CL) and average linkage (AL), are examined. A cooperative framework of SL, CL and AL is built to select the optimal cluster for tweets wherein the notion of optimal-cluster selection is operationalized using majority voting. The hierarchical clustering techniques are also compared with k-means and two state-of-the-art classifiers (SVM and Naïve Bayes). The performance of clustering and classification is measured in terms of accuracy and time efficiency. The experimental results indicate that cooperative clustering based on majority voting approach is robust in terms of good quality clusters with tradeoff of poor time efficiency. The results also suggest that the accuracy of the proposed clustering framework is comparable to classifiers which is encouraging. INDEX TERMS Cooperative clustering, majority voting, sentiment analysis, twitter sentiment analysis.

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A multiple clustering combination approach based on iterative voting process

Cited by 21 publications

References 25 publications

GeoWaVe: Geometric median clustering with weighted voting for ensemble clustering of cytometry data

GeoWaVe: Geometric median clustering with weighted voting for ensemble clustering of cytometry data

Multiple classifier system for remotely sensed data clustering

A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis

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