Analysis of clustering and association using data mining technique for elderly health condition dataset

Abstract

<p>Data survey on the elderly health condition in each year aimed to investigate<br />the performance result on the elderly health care and to evaluate the elderly’s<br />health and health promotion. Thus, in analyzing the data, it mainly relied on<br />the mining data technique for the evaluating health condition. This study<br />presented the data analysis by clustering method. Then, the data was taken<br />from each group to find the association rule. The analysis results showed that<br />the elderly’s health condition data could be classified into four different<br />groups; cluster 1 (25%) were male elderly with high blood pressure and<br />smoking cigarette, cluster 2 (25%) were female elderly with no the congenital<br />disease but the result from the eye sight examination, it was found that they<br />were long-sighted, cluster 3 (24%) were female elderly with no the congenital<br />disease but having the insomnia and osteoarthritis and cluster 4 (26%) were<br />female elderly with high blood pressure and diabetes. It also indicated that<br />each group had the rule showing the correlation between the data in each<br />group having the minimum value of confidence at 0.8 and the minimum value<br />of support not less than 0.5.</p>

Balanced clustering for student admission school zoning by parameter tuning of constrained k-means

“…This process involves partitioning data to form distinct groups known as clusters, where similar and interconnected data points are grouped together. The primary goal of clustering is to facilitate the organization and analysis of data, enabling deeper insights and more effective decision-making [10]- [12].…”