An analysis of efficient clustering methods for estimates similarity measures

Jagatheeshkumar, G.; Brunda, S. Selva

doi:10.1109/icaccs.2017.8014710

Cited by 4 publications

(3 citation statements)

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“…11. Clustering techniques are a type of unsupervised learning method that can group data into different clusters based on their similarity or distance [73]. Clustering techniques can be used for biosignal processing to segment, classify, or analyze biosignals without prior knowledge or labels [74].…”

Section: B Unsupervised Learning Methods For Biosignal Processingmentioning

confidence: 99%

Machine Learning Approaches in Bioengineering for Biosignal Processing

Biçer,

shayea

2023

Preprint

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This survey paper offers a comprehensive review of the recent advances and applications of Machine Learning (ML) approaches in the interdisciplinary field of bioengineering, specifically in the realm of biosignal processing. Biosignals, including electroencephalograms (EEG), electrocardiograms (ECG), and electromyograms (EMG), are inherently complex, presenting significant challenges such as noise, artifacts, variability, and nonlinearity in their processing. However, ML has shown promise in overcoming these hurdles, enabling the extraction of useful features and insights from these signals. The paper outlines how ML is leveraged for processing, analyzing, classifying, and interpreting biosignals for various applications, such as diagnosis, monitoring, rehabilitation, and brain-computer interfaces. Additionally, it discusses the ongoing challenges and potential future directions of ML applications in this field. Through this review, we aim to highlight the critical role of ML in enabling adaptive, personalized, and intelligent systems that interact with biosignals in real-time, with potential implications for improving patient outcomes in various medical conditions.

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Section: B Unsupervised Learning Methods For Biosignal Processingmentioning

confidence: 99%

Machine Learning Approaches in Bioengineering for Biosignal Processing

Biçer,

shayea

2023

Preprint

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“…M. S. Premkumar and S. H. Ganesh [9] have proposed a work on novel median based initial centroids have been generated and imposed onto an experimental dataset to analyze the performance of the proposed work. The results have shown that the proposed work, improved the accuracy of clustering with reduced number of iterations.…”

Section: Literature Surveymentioning

confidence: 99%

Ensemble Hybrid K- Means and DBSCAN Clustering Algorithm – HDKA for Cancer Dataset

Sangeetha,

Kousalya

2019

IJRTE

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Data Mining is the foremost vital space of analysis and is pragmatically utilized in totally different domains, It becomes a highly demanding field because huge amounts of data have been collected in various applications. The database can be clustered in more number of ways depending on the clustering algorithm used, parameter settings and other factors. Multiple clustering algorithms can be combined to get the final partitioning of data which provides better clustering results. In this paper, Ensemble hybrid KMeans and DBSCAN (HDKA) algorithm has been proposed to overcome the drawbacks of DBSCAN and KMeans clustering algorithms. The performance of the proposed algorithm improves the selection of centroid points through the centroid selection strategy.For experimental results we have used two dataset Colon and Leukemia from UCI machine learning repository.

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“…A despeito da existência de diversos estudos [8,9,10,11,12,13,14,15,16] que avaliaram a eficácia de índices de similaridade aplicados à operação de agrupamento de objetos textuais, a presente análise estende estes trabalhos mediante o exame empírico de cinco índices de semelhança distintos, com o emprego de seis índices de validação de resultados. Em particular, os índices de similaridade distância Euclidiana, distância do coseno, distância de Hamming, coeficiente de Jaccard estendido e coeficiente de correlação de Pearson, foram utilizados para realizar o agrupamento de nove conjuntos de documentos de diferentes extensões e características, com a aplicação do método de particionamento k-means.…”

Section: Introductionunclassified

Avaliação da performance de índices de similaridade aplicados ao agrupamento de objetos textuais

Neto¹,

Negreiros

2017

RBCA

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Resumo: A captura e o armazenamento de dados em formato digital têm permitido às organizações o acúmulo de um volume de informações extremamente elevado, constituído em maior proporção por dados em formato não estruturado, representados por textos. Neste contexto, as atividades de análise de agrupamentos ou classificação não supervisionada de objetos, se constituem como uma das técnicas de mineração de informações mais frequentemente empregadas no intuito de proporcionar a organização do volume progressivamente crescente de elementos textuais, por meio da disposição dos documentos em grupos de itens semelhantes com base em um índice de similaridade. Neste sentido, este estudo avalia os índices de similaridade distância Euclidiana, distância do coseno, distância de Hamming, coeficiente de Jaccard estendido e coeficiente de correlação de Pearson, sob a perspectiva de seis índices de validação de agrupamentos, observando que a distância do coseno representa, conforme a presente análise, o índice de similaridade mais apropriado ao agrupamento de objetos textuais, convertidos em formato estruturado por intermédio de técnicas de mineração de textos.Palavras-chave: Análise de agrupamentos. Agrupamento de documentos. Índices de similaridade. Abstract:The capture and the digital data store have allowed companies the accumulation of an extremely high volume of information, constituted mainly by unstructured data, represented by texts. In this context, the cluster analysis operations or unsupervised classification of objects, represent one of the most frequently used data mining techniques to provide the organization of the progressively increasing volume of textual elements, by means of arrangement of the documents in groups of similar itens based in a similarity measure . In this sense, this article evaluate the similarity measures Euclidiean distance, cosine distance, Hamming distance, extended Jaccard coefficient and Pearson's correlation coefficient, from the perspective of six clustering validation indexes, noticing that the cosine distance represent, according to this analysis, the similarity measure most appropriate to clustering textual objects, converted into structured format through text mining techniques.Keywords: Clustering analysis. Document clustering. Similarity index 1 Introdução A mineração de dados é um processo de descoberta automática de conhecimento em grandes repositórios de dados. Correspondente a um conjunto de técnicas que atuam sobre grandes bancos de dados a fim de identificar padrões úteis que, de outra forma, permaneceriam desconhecidos. As tarefas da mineração de dados são classificadas em duas categorias principais: tarefas de previsão e tarefas descritivas. As tarefas de previsão têm como objetivo prever o conteúdo de um determinado atributo, nomeado como a variável dependente ou alvo, com base nos valores de outros atributos, conhecidos como variáveis independentes ou explicativas. Já as tarefas descritivas

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An analysis of efficient clustering methods for estimates similarity measures

Cited by 4 publications

References 5 publications

Machine Learning Approaches in Bioengineering for Biosignal Processing

Machine Learning Approaches in Bioengineering for Biosignal Processing

Ensemble Hybrid K- Means and DBSCAN Clustering Algorithm – HDKA for Cancer Dataset

Avaliação da performance de índices de similaridade aplicados ao agrupamento de objetos textuais

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