K-Means Cluster Analysis Based on Consumer Behavior

Cai, Kaiming; Rodavia, Maria Rosario D.

doi:10.1109/aiam57466.2022.00034

Cited by 2 publications

(6 citation statements)

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“…The clustering analysis was performed by applying two hierarchical clustering methods (agglomeration and division) and Ward's approach based on Euclidean distance and k-means [34]. We used the NbClust R package to explore the optimal number of clusters in our dataset by varying all combinations of cluster number, distance measure, and clustering method and considered the Elbow method (k = 4) and Scott index (k = 4) for optimal cluster selection [35] (Appendix 2). Finally, the number of clusters (k = 4) was selected by visually inspecting the data (clusters 1, 2, 3, and 4).…”

Section: Ethical Approvalmentioning

confidence: 99%

“…After selecting the number of clusters, clusters were formed by repeating the K-means algorithm, which is most commonly used in unsupervised machine learning techniques, until the center value of the cluster did not change. Once the clusters were identified [35], a radar chart was created to explore the functionality of the final clusters (Figure 1).…”

Section: Ethical Approvalmentioning

confidence: 99%

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Eating Habits and Lifestyle Factors Related to Childhood Obesity Among Children Aged 5-6 Years: Cluster Analysis of Panel Survey Data in Korea

Lim,

Lee

2024

JMIR Public Health Surveill

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Background Childhood obesity has emerged as a major health issue due to the rapid growth in the prevalence of obesity among young children worldwide. Establishing healthy eating habits and lifestyles in early childhood may help children gain appropriate weight and further improve their health outcomes later in life. Objective This study aims to classify clusters of young children according to their eating habits and identify the features of each cluster as they relate to childhood obesity. Methods A total of 1280 children were selected from the Panel Study on Korean Children. Data on their eating habits (eating speed, mealtime regularity, consistency of food amount, and balanced eating), sleep hours per day, outdoor activity hours per day, and BMI were obtained. We performed a cluster analysis on the children’s eating habits using k-means methods. We conducted ANOVA and chi-square analyses to identify differences in the children’s BMI, sleep hours, physical activity, and the characteristics of their parents and family by cluster. Results At both ages (ages 5 and 6 years), we identified 4 clusters based on the children’s eating habits. Cluster 1 was characterized by a fast eating speed (fast eaters); cluster 2 by a slow eating speed (slow eaters); cluster 3 by irregular eating habits (poor eaters); and cluster 4 by a balanced diet, regular mealtimes, and consistent food amounts (healthy eaters). Slow eaters tended to have the lowest BMI (P<.001), and a low proportion had overweight and obesity at the age of 5 years (P=.03) and 1 year later (P=.005). There was a significant difference in sleep time (P=.01) and mother’s education level (P=.03) at the age of 5 years. Moreover, there was a significant difference in sleep time (P=.03) and the father’s education level (P=.02) at the age of 6 years. Conclusions Efforts to establish healthy eating habits in early childhood may contribute to the prevention of obesity in children. Specifically, providing dietary guidance on a child’s eating speed can help prevent childhood obesity. This research suggests that lifestyle modification could be a viable target to decrease the risk of childhood obesity and promote the development of healthy children. Additionally, we propose that future studies examine long-term changes in obesity resulting from lifestyle modifications in children from families with low educational levels.

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Section: Ethical Approvalmentioning

confidence: 99%

Section: Ethical Approvalmentioning

confidence: 99%

Eating Habits and Lifestyle Factors Related to Childhood Obesity Among Children Aged 5-6 Years: Cluster Analysis of Panel Survey Data in Korea

Lim,

Lee

2024

JMIR Public Health Surveill

View full text Add to dashboard Cite

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“…Least Squares Estimation [9][10][11][12][13][14][15] optimizes the proposed model. Chebyshev functional polynomial [16][17][18][19][20][21][22] are engendered as,…”

Section: Mathematical Design Of Teaching By Master To Adolescent Stud...mentioning

confidence: 99%

“…Initially, a set of contestant solutions arbitrarily engendered in the exploration space subsequently, every tender appraised, and subsequently its price function computed. Psychological search is one of the key operators in the algorithm and it explores the environs of Volume 13 | Issue 1 | 2485 every tender by using a Levy flight division (distribution) [22]. Key objective of levy flight distribution is that it engenders numerous petite steps and afterwards an extensive jump will performed.…”

Section: Mathematical Design Of Teaching By Master To Adolescent Stud...mentioning

confidence: 99%

“…Probabilistic factor is set up through k-means. Therefore, in the projected clustering approach, k-means [22] not implemented in iterations. A new-fangled tender engendered by stirring the previous tender towards the excellent tender in the conqueror cluster It is merit to declare that the excellent tender in the conqueror cluster is not essentially the most excellent bid in the populace of tender.…”

Section: Mathematical Design Of Teaching By Master To Adolescent Stud...mentioning

confidence: 99%

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Scientific Modelling of Teaching by Master to Adolescent Students for Personality Improvement, Relationship and Vividness for Improving the Electrical Energy Quality

Kanagasabai

2023

Int. J. Autom. Smart

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In this paper Scientific Modelling of Teaching by Master to Adolescent students for Personality Improvement, Relationship and Vividness (TMASPRV) algorithm applied to the engineering domain problem in order to increase the power productivity by Improving the Electrical Energy Quality. Each Adolescent students fitness value is computed by utilizing (substituting) the characteristics (decision variables) in the standard fitness. An estimated model between the individuals and their fitness values created based on Chebyshev functional-link network. By Least Squares Estimation, the proposed model optimized. Analogous to selecting preliminary populace, selecting the preeminent solution in the new population to the role of the Prime Teacher, rendering to all the other Teacher and Student's -location is significant. This choice will regulate the convergence rapidity as well as the accurateness of the procedure. Therefore, the procedure's principal phase is to discover an operative solution to play a protagonist of the preeminent solution to upsurge the convergence and accurateness of the exploration iterations. In the segment of Self-experiences (local search) every Human being has assertiveness in the direction of each factor in the on-going life. Mathematical Design of Teaching by Master to Adolescent students for Personality Improvement, Relationship and Vividness (TMASPRV) algorithm validated in benchmark test functions. In engineering domain Electrical problem projected TMASPRV algorithm performed well in reducing the electrical real power loss.

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K-Means Cluster Analysis Based on Consumer Behavior

Cited by 2 publications

References 0 publications

Eating Habits and Lifestyle Factors Related to Childhood Obesity Among Children Aged 5-6 Years: Cluster Analysis of Panel Survey Data in Korea

Eating Habits and Lifestyle Factors Related to Childhood Obesity Among Children Aged 5-6 Years: Cluster Analysis of Panel Survey Data in Korea

Scientific Modelling of Teaching by Master to Adolescent Students for Personality Improvement, Relationship and Vividness for Improving the Electrical Energy Quality

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