Analysis of several decision fusion strategies for clustering validation. Strategy definition, experiments and validation

Yera, Ainhoa; Arbelaitz, Olatz; Jodra, Jose Luis; Gurrutxaga, Ibai; Pérez, Jesús M.; Muguerza, Javier

doi:10.1016/j.patrec.2016.11.009

Cited by 6 publications

(8 citation statements)

References 10 publications

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“…Since there is no universal CVI to always make a correct decision, many authors [44,69] agreed to use multi-criteria solutions to reach the best and adequate results. Multi-criteria solutions assume the adoption of several CVIs to achieve greater certainty and correctness of clustering results.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, Kryszczuk and Hurley [44] pointed that the best-performing scheme was the mean-rule decision fusion scheme. The recent work by Yera et al [69] also discusses the use of decision fusion strategies for cluster validation purposes. The authors suggested two types of voting strategies: Global Voting and Selective Voting.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, three approaches were developed, each limiting the group of voting CVIs based on the following criteria: the global performance of the CVIs, their factor dependence success rate, and the impact the CVIs have on the results. The Yera et al [69] decision fusion techniques presented above have certain critical disadvantages. The Global Voting approach was not even used in the comparative study due to the weakness of the archived results.…”

Section: Related Workmentioning

confidence: 99%

“…In the recent work by Gurrutxaga et al [33], the authors accepted that there is no single way of establishing the quality of a partition by selecting the optimal validation index which would be more robust than the rest in all contexts and under different conditions. Therefore, Yera et al [69] suggest using decision fusion validation strategies to obtain a more stable behavior which would make it possible for the user to avoid having to choose a different validation index for each particular environment.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, the authors drew inspiration from the works of Arbelaitz et al [2], Gurrutxaga et al [33], Yera et al [69], and the decision fusion method developed by Charrad et al [16]. Since Charrad et al [16] did not substantiate the use of a particular data, clustering algorithm, and a dissimilarity measure for the majoritybased decision fusion voting procedure, the authors provide a comparative study of these factors.…”

Section: Introductionmentioning

confidence: 99%

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A Step Towards the Majority-Based Clustering Validation Decision Fusion Method

Panskyi

Mosorov

2021

IAPGOS

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A variety of clustering validation indices (CVIs) aimed at validating the results of clustering analysis and determining which clustering algorithm performs best. Different validation indices may be appropriate for different clustering algorithms or partition dissimilarity measures; however, the best suitable index to use in practice remains unknown. A single CVI is generally unable to handle the wide variability and scalability of the data and cope successfully with all the contexts. Therefore, one of the popular approaches is to use a combination of multiple CVIs and fuse their votes into the final decision. The aim of this work is to analyze the majority-based decision fusion method. Thus, the experimental work consisted of designing and implementing the NbClust majority-based decision fusion method and then evaluating the CVIs performance with different clustering algorithms and dissimilarity measures in order to discover the best validation configuration. Moreover, the author proposed to enhance the standard majority-based decision fusion method with straightforward rules for the maximum efficiency of the validation procedure. The result showed that the designed enhanced method with an invasive validation configuration could cope with almost all data sets (99%) with different experimental factors (density, dimensionality, number of clusters, etc.).

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Step Towards the Majority-Based Clustering Validation Decision Fusion Method

Panskyi

Mosorov

2021

IAPGOS

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New internal clustering validation measure for contiguous arbitrary‐shape clusters

Rojas-Thomas

Santos

2021

Int J Intell Syst

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In this study a new internal clustering validation index is proposed. It is based on a measure of the uniformity of the data in clusters. It uses the local density of each cluster, in particular, the normalized variability of the density within the clusters to find the ideal partition. The new validity measure allows it to capture the spatial pattern of the data and obtain the right number of clusters in an automatic way. This new approach, unlike the traditional one that usually identifies wellseparated compact clouds, works with arbitrary-shape clusters that may be contiguous or even overlapped.The proposed clustering measure has been evaluated on nine artificial data sets, with different cluster distributions and an increasing number of classes, on three highly nonlinear data sets, and on 17 real data sets. It has been compared with nine well-known clustering validation indices with very satisfactory results. This proves that including density in the definition of clustering validation indices may be useful to identify the right partition of arbitrary-shape and different-size clusters.

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Statistical and clustering validation analysis of primary students' learning outcomes and self-awareness of information and technical online security problems at a post-pandemic time

Panskyi

Korzeniewska

2022

Educ Inf Technol

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The authors decided to investigate the impact of the pandemic period and the resulting limitations in Polish primary school online security education. The first part of the study investigates the impact of the COVID-19 pandemic on students’ educational learning outcomes in information and Internet security. The study has been performed via a student-oriented survey of 20 questions. The statistical analysis confirms the significant difference before and after the pandemic in several questions at most. Nevertheless, this justifies the statement that pandemics had a positive impact on post-pandemic Internet-related security education. The second part of the study has been focused on students' perception and self-awareness of cyberspace problems. For this purpose, the authors used novel majority-based decision fusion clustering validation methods. The revealed results illustrate the positive tendency toward the students' self-awareness and self-confidence of online security problems and e-threats before, during and after the challenging pandemic period. Moreover, the presented validation methods show the appealing performance in educational data analysis, and therefore, the authors recommended these methods as a preprocessing step that helps to explore the intrinsic data structures or students' behaviors and as a postprocessing step to predict learning outcomes in different educational environments.

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Analysis of several decision fusion strategies for clustering validation. Strategy definition, experiments and validation

Cited by 6 publications

References 10 publications

A Step Towards the Majority-Based Clustering Validation Decision Fusion Method

A Step Towards the Majority-Based Clustering Validation Decision Fusion Method

New internal clustering validation measure for contiguous arbitrary‐shape clusters

Statistical and clustering validation analysis of primary students' learning outcomes and self-awareness of information and technical online security problems at a post-pandemic time

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