Data Preprocessing in Non-Hierarchical Cluster Analysis for Characteristics of Classification Results

Abstract

Data science is being increasingly emphasized in university education, and student are required to develop the ability to handle data correctly and address complex real-world challenges. In recent years, the use of tools such as generative AI has created an environment where even beginners can easily perform data analysis. Among these techniques, cluster analysis, which can reveal data patterns by grouping similar data points, is widely used. However, the choice of appropriate data preprocessing methods is difficult for beginners, as the results can vary significantly depending on the preprocessing method used. Therefore, this research focuses on the relationship between data preprocessing patterns and analysis results in cluster analysis. By clarifying the characteristics of this relationship, we aim to support beginners in selecting appropriate preprocessing methods. Specifically, we will use financial data from 80 listed companies, apply four different preprocessing patterns, and then perform cluster analysis to identify the differences in results and characteristics that emerge.