Construction of a Comprehensive Analysis Platform for Typology and its Application

Abstract

There is an ongoing effort to consolidate existing elements in various fields such as government, medicine, and education. At the same time, society is facing considerable challenges, such as a projected aging of the population, a decline in the younger population, and a shortage of workers. To respond to these issues, it is necessary to classify the features of the objects in each field and to propose efficient improvements. In this research, we propose a structure and flow for a classification analysis foundation, as well as an environment in which classifications can be performed on any scale. A data envelopment analysis (DEA) is used for the classification in order to clarify the features of each element. We then use the analysis foundation to classify medical and educational institutions in order to investigate the type of categories produced. Various approaches have been used for classification. Methods used in cluster analyses include the farthest neighbor method and the k-means method. However, because these methods use average values, it is possible that the features of individual elements may remain unclear. Moreover, classification foundations include a geographically specialized foundation, which does not lend itself to general use. Furthermore, many classification analyses restrict the number of objects, casting doubt on their ability to cope with large-scale classification analyses. In this research, we propose an analysis foundation that is capable of general-purpose, large-scale classifications. The classification analysis foundation is structured to enable the classification of many objects, including medical institutions, educational institutions, and patient analyses. Here, APIs are used to provide the subject data for the classifications, and a DEA is used for the calculations to clarify the features of the objects.