Self-organized Clustering and Classification: A Unified Approach via Distributed Chaotic Computing

Benderskaya, Elena N.; Zhukova, Sofya V.

doi:10.1007/978-3-642-19934-9_54

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…22 Along with a t-SNE graph, a Sankey diagram was drawn to present the flow direction between the 2018 EFP/AAP classification, periodontitis clusters, and the 2012 CDC/AAP definition. To verify the robustness of the optimal number of clusters, the Fuzzy C-means algorithm (FCM) 23 was further applied to estimate the number of clusters. The FCM is a method of clustering that allows one piece of data to belong to ≥2 clusters.…”

Section: Unsupervised Clustering and Reclassificationmentioning

confidence: 99%

Assessing the surveillance use of 2018 EFP/AAP classification of periodontitis: A validation study and clustering analysis

et al. 2023

Journal of Periodontology

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BackgroundThe performance of the 2018 European Federation of Periodontology/American Academy of Periodontology (EFP/AAP) classification of periodontitis for epidemiology surveillance purposes remains to be investigated. This study assessed the surveillance use of the 2018 EFP/AAP classification and its agreement with the unsupervised clustering method compared with the 2012 Centers for Disease Control and Prevention(CDC)/AAP case definition.MethodsParticipants (n = 9424) in the National Health and Nutrition Examination Survey (NHANES) were staged by the 2018 EFP/AAP classification and classified into subgroups via k‐medoids clustering. Concordance levels between periodontitis definitions and the clustering method were evaluated through the multiclass area under the receiver operating characteristic curve (multiclass AUC) among “periodontitis cases” and the general population, respectively. The multiclass AUC of the 2012 CDC/AAP definition versus clustering was used as a reference. The associations of periodontitis with chronic diseases were estimated using multivariable logistic regression.ResultsAll the participants were identified as “periodontitis cases” by the 2018 EFP/AAP classification, and the prevalence of stage III–IV was 30%. The optimal numbers of clusters were three and four. The 2012 CDC/AAP definition versus clustering yielded a multiclass AUC of 0.82 and 0.85 among the general population and “periodontitis cases,” respectively. The multiclass AUC of the 2018 EFP/AAP classification versus clustering was 0.77 and 0.78 for different target populations. Similar patterns prevailed in associations with chronic diseases between the 2018 EFP/AAP classification and clustering.ConclusionsThe validity of the 2018 EFP/AAP classification was verified by the unsupervised clustering method, which performed better in distinguishing “periodontitis cases” than classifying the general population. For surveillance purposes, the 2012 CDC/AAP definition showed a higher agreement level with the clustering method than the 2018 EFP/AAP classification.

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Section: Unsupervised Clustering and Reclassificationmentioning

confidence: 99%

Assessing the surveillance use of 2018 EFP/AAP classification of periodontitis: A validation study and clustering analysis

et al. 2023

Journal of Periodontology

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“…Fuzzy clustering extends this concept to associate each pattern with every cluster using a membership function. The most widely used clustering algorithm implementing the fuzzy philosophy is FCM, initially developed by Dunn and later generalized by Bezdek [4], who proposed a generalization by means of a family of objective functions. The basic structure of the FCM algorithm is discussed below.…”

Section: B the Fuzzy C-means Algorithmmentioning

confidence: 99%

Evaluation of k-Medoids and Fuzzy C-Means clustering algorithms for clustering telecommunication data

Velmurugan¹

2012

2012 International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET)

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Data mining approach and its technology is used to extract the unknown pattern from the large set of data for the business as well as real time applications. This research work deals with two of the most delegated, partition based clustering algorithms in data mining namely k-Medoids and Fuzzy C-Means. These two algorithms are implemented and the performance is analyzed based on their clustering result quality. The connection oriented broad band data is the source of data for this analysis. To test the performance, the distance between the server locations and their connections are taken for clustering. The number of connections in the servers is changed after the clustering process. The run time for each algorithm is analyzed and the results are compared with one another. Finally, the best algorithm is suggested based on their computational time for the chosen telecommunication data.

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“…The fuzzy clustering method is a methodological approach in cluster analysis that is not commonly used to measure consumer behavior due to its operative complexity, but it can help in the precise segmenting of global consumer perceptions that vary according to the culture they belong to (Punj and Stewart, 1983; Wedel and Kamakura, 2000). The main advantage of fuzzy clustering is that a single (individual) point belongs to different groups simultaneously, but with different degrees of belonging (Benderskaya and Zhukova, 2011; Velmurugan, 2014; Velmurugan and Santhanam, 2010). It should be noted that this methodological approach is widely used in a number of sciences including data mining, machine learning and image processing among others (Bezdek and Pal, 1992; Yang et al , 2002).…”

Section: Introductionmentioning

confidence: 99%

An international market segmentation model based on susceptibility to global consumer culture

Merino

Lazo

Talavera

et al. 2020

CCSM

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PurposeCompanies that wish to market a global brand need to develop a greater understanding of consumers' and potential consumers' susceptibility to global consumer culture (SGCC) with a view to standardizing/adapting their brand according to the desires and preferences of the consumers who belong to specific segments of global consumers. Thus, the aim of the study is to fill a joint segmentation research gap within and between countries based on seven dimensions of SGCC while classifying consumers according to the degree of belonging to specific and hybrid (global citizenship) segments.Design/methodology/approachA questionnaire was applied online in English in five countries across the Americas and Europe resulting in a sample of 412 consumers. Based on the fuzzy C-means cluster analysis, the study segments the sample of consumers according to the degree of belonging to specific and global citizenship segments.FindingsAnalysis of survey results show three groups; two distinct groups and a third with features of both, a distinct intersection group. These findings suggest that consumers in different countries develop beliefs and attitudes about global citizenship, and this perspective coincides with the characteristics of the intersection group. Consequently, the study shows that fragmentation of the needs of consumers exists within and between countries.Originality/valueThis study contributes to the concept of global citizenship, helping managers of global brands improve their marketing strategy decisions by implementing strategies that are standardized or adapted to specific hybrid segments of consumers that transcend national borders. This study used a statistical method to measure the degree of belonging to each segment.

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Self-organized Clustering and Classification: A Unified Approach via Distributed Chaotic Computing

Cited by 6 publications

References 11 publications

Assessing the surveillance use of 2018 EFP/AAP classification of periodontitis: A validation study and clustering analysis

Assessing the surveillance use of 2018 EFP/AAP classification of periodontitis: A validation study and clustering analysis

Evaluation of k-Medoids and Fuzzy C-Means clustering algorithms for clustering telecommunication data

An international market segmentation model based on susceptibility to global consumer culture

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