Comparison of the Performance of Several Regression Algorithms in Predicting the Quality of White Wine in WEKA

Clarin, Jeffrey A.

doi:10.46338/ijetae0722_03

Cited by 33 publications

(3 citation statements)

References 11 publications

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“…The K-means algorithm (Sharma et al, 2012) is applied, using Manhattan distances, available in WEKA software, which is the sum of the absolute differences between points in all dimensions (Kubat, 2017). In this regard, the K-means algorithm performs the following steps: (i) K points are located in space representing the objects to be grouped.…”

Section: Methodsmentioning

confidence: 99%

Digital Technologies 4.0 in Small and Medium-Sized Manufacturing Industries: Cases of the Central Region of Argentina and the Biobio Region of Chile

Gatica-Neira,

Ramos-Maldonado,

Andrés Ascua

et al. 2024

Sage Open

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The Fourth Industrial Revolution has built upon the lessons learned from Covid-19 and has emphasized the urgent need for digital transformation in Small and Medium Enterprises (SMEs) in Latin America. This study aims to identify the factors that can explain the varying degrees of adoption of digital technologies 4.0 (DT 4.0). The research analyzes the results of surveys conducted among 35 companies located in the Central Region of Argentina and the Biobio Region of Chile. By utilizing the Technology, Organization, and Environment (TOE) model of adoption and employing data science tools, such as cluster analysis (K-means) and decision trees (J48), the study parameterizes the different responses and generates valuable insights. Our work provides a complementary view to existing studies, analyzing the key factors in the adoption of DT 4.0 in a group of SMEs located in regions, not national capitals, of two Latin American countries with different growth models. The results highlight the significance of business leaders possessing knowledge of DT 4.0, the importance of having specialized human capital, and the need for an organizational culture that embraces innovation. Public policy should focus on transforming business leadership and organizational dynamics to stimulate digital transformation. JEL Classification: O330, O140, O570

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Section: Methodsmentioning

confidence: 99%

Digital Technologies 4.0 in Small and Medium-Sized Manufacturing Industries: Cases of the Central Region of Argentina and the Biobio Region of Chile

Gatica-Neira,

Ramos-Maldonado,

Andrés Ascua

et al. 2024

Sage Open

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“…Farthest-point heuristicbased method has the time complexity of O(nk), where n denotes the number of data objects in the dataset and k represent the number of desired clusters. The farthest-point clustering technique is particularly suitable and fast for data mining applications in large scale (Sharma, Bajpai, & Litoriya, 2012).…”

Section: 3mentioning

confidence: 99%

The Effects of Dimensionality Reduction in the Classification of Network Traffic Datasets Via Clustering

Maryam,

Adeleke Raheem

2020

JASIC

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Unsupervised learning has emerged as an alternative meta-learning approach that is capable of accurately classifying the massive amount of data generated by modern-day applications. It is useful for active monitoring and provision of improved service quality by the network administrators. Extracting the optimal and most essential features with high discriminative power remains one of the critical challenges in unsupervised learning due to the absence of the class labels. The main objective of this research is to determine the effects of Dimensionality Reduction in Feature Selection via the clustering of internet traffic data sets. To achieve this overall goal, internet traffic data sets were retrieved, analyzed and clustered into application classes. A reduced form of these datasets was obtained and clustered using feature selection techniques. The results of the original and reduced data sets were compared and evaluated. The effects of two feature reduction techniques; Correlation-based Feature Selection (CFS) and Information Gain Attribute Evaluator were examined in K-means, Expectation Maximization and the Farthest-first clustering algorithms. The effectiveness of the candidate clustering algorithms was determined and the evaluation was based on overall accuracy, precision, recall, and Receiver Operating Characteristic (ROC) area metrics. Results revealed that both CFS and information gain significantly increase the performance of the three algorithms.

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“…The goal is that objects in a group are similar (or related) to each other and different (or unrelated) from objects in other groups. The greater the similarity within a group and the greater the disparity between groups, the better the clustering [26]. However, K-means is the essential and fundamental technique through which data centres are analysed.…”

Section: Introductionmentioning

confidence: 99%

Segmenting the eating behaviour of university students using the K-means algorithm

2023

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Universities that teach arts education do not only teach how to play an instrument or conduct musical ensembles; they are agents of change in eating behaviour for the praxis of teaching and dissemination of healthy education. The objective of the research was to segment the eating behaviour of students of the artistic education-music speciality of the National University of Education "Enrique Guzmán y Valle" by applying the K-means algorithm. To do this, the methodology consisted of understanding the problem, understanding the data collected, preparing the data, modelling and evaluating the model. For modelling, the free software Weka was used through the K-means clustering technique on a data matrix of 148 instances with forty-three nominal variables collected online based on an instrument designed and validated to assess eating behaviour in university students. Two was determined to be the optimal clustering for eating behaviour in university students, using the elbow method, with a distribution of 49% for the first cluster and 51% for the second cluster. The results of the study population showed that the eating behaviour of university students is adequate.

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Comparison of the Performance of Several Regression Algorithms in Predicting the Quality of White Wine in WEKA

Cited by 33 publications

References 11 publications

Digital Technologies 4.0 in Small and Medium-Sized Manufacturing Industries: Cases of the Central Region of Argentina and the Biobio Region of Chile

Digital Technologies 4.0 in Small and Medium-Sized Manufacturing Industries: Cases of the Central Region of Argentina and the Biobio Region of Chile

The Effects of Dimensionality Reduction in the Classification of Network Traffic Datasets Via Clustering

Segmenting the eating behaviour of university students using the K-means algorithm

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