A Stochastic Multi-criteria divisive hierarchical clustering algorithm

Ishizaka, Alessio; Lokman, Banu; Tasiou, Menelaos

doi:10.1016/j.omega.2020.102370

Cited by 41 publications

(15 citation statements)

References 66 publications

(64 reference statements)

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“…All parameters of the second group are transformed with one-hot encoding technique (or dummy variables are created) 8,9 .…”

Section: Resultsmentioning

confidence: 99%

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The impact of e-learning and social parameters on students’ academic performance

Petrusevich¹

2020

Science for Education Today

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Introduction. The article examines the problem of assessing students' academic performance in the current situation. The purpose of the paper is to evaluate the influence of e-learning and some social and behavioral parameters on students’ academic performance. Materials and Methods. The author employed the machine learning procedures in order to identify and assess the current problems of the educational system, students’ behavior, and universities’ policy. Methods of mathematical analysis and statistics as well as ensemble methods (gradient boosting and the random forest algorithms) were used in order to achieve high accuracy of the research. Results. The author conducted the analysis of the following datasets devoted to academic performance at higher and secondary educational institutions in a number of countries: Students’ Performance in Portugal, E-learning Student Reactions and Students’ Academic Performance. The purposes of the current study were to identify statistical correlations between social parameters of students and the level of their academic performance and to understand how academic performance is determined by the implementation of online learning and blended learning. The research findings suggest that mathematical statistics and data analysis methods allow to identify correlations between students’ performance data and reveal hidden relationships which can be important for university staff. Conclusions. In conclusion, the author summarizes the results of evaluating the impact of the introduction of e-learning elements and some social parameters on students’ academic performance. Keywords Clustering students; Blended learning; Academic performance evaluation; Digitalization of education; Digital technologies in education; Correlation of features; performance improvement.

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“…All parameters of the second group are transformed with one-hot encoding technique (or dummy variables are created) 8,9 .…”

Section: Resultsmentioning

confidence: 99%

“…In the expression (1) x is a source value, x' is a transformed version of this parameter, µ is mean value of x and σ is its standard deviation 8,9 .…”

Section: Resultsmentioning

confidence: 99%

The impact of e-learning and social parameters on students’ academic performance

Petrusevich¹

2020

Science for Education Today

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“…The method of splitting hierarchical clustering begins with a single cluster comprising all data items. Following that, with each iteration, it separates into clusters by meeting certain resemblance requirements, before every piece of information entity creates its cluster or the stopping criteria are reached [44,45]. • Expectation and maximization (EM) algorithm EM an unphased algorithm for estimating the maximum likelihood values of variables in unstructured statistical models When it runs the expectation stage, it will calculate variables that maximize the assumptions discovered during the E phase; therefore, it must perform a maximization step in order to search for new ones.…”

Section: Stepsmentioning

confidence: 99%

COVID-19 World Vaccine Adverse Reactions Based on Machine Learning Clustering Algorithm

Abdulrahman

Abdulazeez

Hasan

2021

QAJ

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In December 2019, a novel coronavirus, now named SARS-CoV-2, caused a series of acute atypical respiratory diseases in Wuhan, Hubei province, China. It triggered several acute atypical respiratory diseases. COVID-19 was the name given to the virus's disease. The infection is human-to-human transmissible, and it has triggered a global pandemic. Vaccines against COVID-19 are an essential global intervention to control the current pandemic situation and in a fairly short time, several vaccines have been developed to try to control the situation but have also led to consequences in the form of adverse reactions. Clustering algorithms have been used in computational intelligence and digital analysis, which is one of the areas that has taken this into account. Clustering can be described as a method of grouping similar data into one population or cluster and separating unrelated data into another. For clustering COVID-19 vaccine adverse reactions datasets, a variety of clustering algorithms are used. The objective of this paper is to use the clustering algorithms used in the case of COVID-19 Vaccine Adverse Reactions datasets, demonstrating how these algorithms help to provide accuracy for clustering the COVID-19 Vaccine Adverse Reactions. This study compared four clustering algorithms using the WEKA tool. Furthermore, it details the datasets in terms of different variables of precision, cluster case, number of iterations, time, and present the findings of these papers, and which clustering algorithms used and the accuracy of these algorithms. It is found that the clustering algorithm k-means is used widely in different types of the COVID-19 vaccine adverse reactions datasets with high accuracy.

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“…Two types of algorithm are often implemented when moving up the hierarchy. The divisive approach of clustering reckons all data as one cluster and performs splits, which is used in many research [8]. Nevertheless, the agglomerative hierarchical clustering is a bottom-up method with many variants [9].…”

Section: Related Workmentioning

confidence: 99%

Coword and Cluster Analysis for the Romance of the Three Kingdoms

Fan

Liu

2021

Wireless Communications and Mobile Computing

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The Romance of the Three Kingdoms (RTK) is a classical Chinese historical novel by Luo Guanzhong. This paper establishes a research framework of analyzing the novel by utilizing coword and cluster analysis technology. At the beginning, we segment the full text of the novel, extracting the names of historical figures in the RTK novel. Based on the coword analysis, a social network of historical figures is constructed. We calculate several network features and enforce the cluster analysis. In addition, a modified clustering method using edge betweenness is proposed to improve the effect of clustering. Finally, both quantified and visualized results are displayed to confirm our approach.

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A Stochastic Multi-criteria divisive hierarchical clustering algorithm

Cited by 41 publications

References 66 publications

The impact of e-learning and social parameters on students’ academic performance

The impact of e-learning and social parameters on students’ academic performance

COVID-19 World Vaccine Adverse Reactions Based on Machine Learning Clustering Algorithm

Coword and Cluster Analysis for the Romance of the Three Kingdoms

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