Crisp and fuzzy cluster validity: Generalized intra-inter silhouette index

Rawashdeh, Mohammad; Ralescu, Anca

doi:10.1109/nafips.2012.6290969

Cited by 18 publications

(8 citation statements)

References 10 publications

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“…The number of clusters for Fuzzy cmeans algorithm is found by using silhouette score. The clustering quality of each data point is measured by constructing a silhouette for that point [34]. The number of the clusters is found as 7.…”

Section: Fuzzy C-meansmentioning

confidence: 99%

Russia-Ukraine Conflict: A Text Mining Approach through Twitter

Eligüzel

2023

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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The focus of this study is to use social media to investigate the Russia-Ukraine conflict. With the assent of the Russian parliament, Russian President Vladimir Putin proclaimed that they will begin invading Ukraine on February 24, 2022. During the Russia-Ukraine conflict, social media, particularly Twitter, has been heavily used. For that reason, it becomes to strong tool for handling processes during the conflict such as political decision making, organizing humanitarian activities, and proving assistance for victims. As a result, social media becomes the most up-to-date, comprehensive, and large information source for current scenario analysis. A total of 65412 tweets are gathered as a dataset for analysis in the proposed study between February 24 and April 5. Then, for each tweet, a topic modeling method called Latent Dirichlet Allocation (LDA) is used to collect significant topics and their probabilities considering each tweets. Then, using the specified probabilities, Fuzzy c-means is utilized to generate clusters for the entire document. Finally, seven unique clusters have been gathered for processing. N-grams and network analysis are used to examine each resulting cluster for a better understanding. As a result of this study, worldwide public opinion, current situation of civilians, course of the conflict, humanitarian issues during the Russia-Ukraine conflict are extracted.

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Section: Fuzzy C-meansmentioning

confidence: 99%

Russia-Ukraine Conflict: A Text Mining Approach through Twitter

Eligüzel

2023

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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“…Para encontrar o número de clusters, foi utilizado o coeficiente Silhueta Intra-Inter Generalizada. Este coeficiente foi proposto por Rawashdeh and Ralescu (2012) é e um caso geral do coeficiente de silhueta apresentado em Rousseeuw (1987). Esta métrica busca avaliar tanto o grau de compacticidade de um agrupamento (chamado de a j ) quanto o grau de separação entre agrupamentos (chamado de b j ),ponderando a distância entre os pontos (d jk ).…”

Section: Silhueta Intra-inter Generalizadaunclassified

Clusterização Fuzzy Otimizada para Estabelecimento de Limites Coletivos dos Indicadores de Continuidade

Thimoteo

Borges

Vellasco

et al. 2020

Anais Do Simpósio Brasileiro De Sistemas Elétricos 2020

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Este trabalho propõe uma nova metodologia de cálculo dos limites coletivos dos indicadores de continuidade das distribuidoras de energia elétrica no Brasil. Esta metodologia tem como base a execução de uma clusterização fuzzy otimizada, utilizando o algoritmos Fuzzy C-Means e de Otimização por Enxame de Partículas, com o intuito de flexibilizar o agrupamento de conjuntos de unidades consumidoras e o cálculo de metas para seus indicadores coletivos de continuidade. A clusterização foi realizada por regiões do Brasil e os resultados obtidos são comparáveis à metodologia atual, porém com uma trajetória de redução mais acentuada.

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“…In other words, the silhouette index measures the degree of confidence in the clustering assignment of a given observation. For an observation i, the silhouette index is defined as Rawashdeh and Ralescu (2012):…”

Section: K-means Clusteringmentioning

confidence: 99%