Using Data Mining Techniques for the Clustering of Elderly People in Taiwan And Analyzing Their Relationships with Life Satisfaction and Life Model Factors

• Main Authors: LIN,JIAN-CHENG, 林建成
• Other Authors: TSAI, CHANG-MING
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/24341399069258835029

碩士 === 長榮大學 === 資訊管理學系碩士班 === 100 === The low birth rate in Taiwan has become increasingly critical in recent years. Due to the improvement in living standard and the advances in medical technology, the lifespan of general population has significantly lengthened. According to the report of the Council for Economic Planning and Development (CEPD) in 2010, the elderly population ratio in 2060 will expect to reach a 41.6% level. At that time, the elderly population will become the single largest age group. As a result, the care of the elderly population in the future will not be affordable for many families. The housing problem of elderly population is expected to become an important issue that the government and the society as a whole must deal with. Today, the elderly population treats the quality of living seriously. Life satisfaction is an important indicator for the quality of living. Hence, the research in housing need for the elderly population is imminent. This study will employ different living styles as segmentation factor to cluster the elderly population. Through the results of cluster analysis, we will discuss their difference with respect to living style and life satisfaction. This is a cross-sectional study that is based upon the data set of 2007 Survey of the Elderly in Taiwan, Bureau of Health Promotion, Department of Health. This study will apply three well-known clustering methods to enhance the suitability of cluster results. They are self-organizing map (SOM), K-means and Dbscan. We will discuss the meaning of associated descriptive statistics further, and conduct some ANOVA and chi-square test procedures with them to investigate the attributes of the elderly population, and to identify the relationship among them. We hope we will be able to distinguish different types of elderly population, to confirm the characteristics of each elderly population, and to differentiate the degree of life satisfaction among clusters. At last, we hope that the results from this study can offer some helpful suggestions to policy makers in government and to nursing home operators as well.