A Case Study for Clustering Characteristics Evaluation and Clustering Dimension Selection by Using UCI News Data

The purpose of this study is to research and explore the clustering algorithm, and provide methods for clustering characteristics evaluation and clustering dimension selection, in order to help the user to understand the impact and meaning of clustering parameters and data dimensions on clustering, thereby strengthening the use of clustering algorithm. In previous studies, many scholars have proposed various types of clustering algorithms. Most of these algorithms need to set the clustering parameters, and the selection of clustering parameters will affect the results after clustering. Therefore, the user must fully understand the meaning of clustering parameters for clustering and select appropriate clustering parameters for clustering, then the clustering algorithm can be effectively used to help solve decision-making problems. Based on the above factors, this study focus on doing further analysis and description on the meaning of the clustering data distribution & the meaning of parameters to the clusters, and the relationship among the clusters, find out the important clustering feature and propose a new clustering evaluation formula, and expect to assist the decision-maker to find appropriate clustering parameters effectively.