The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS

• 作者: Fuhai LENG Loet LEYDESDORFF Qiuju ZHOU
• 来源期刊: 中国文献情报：英文版
• 学科: 工学
• 关键词: 聚类分析 SPSS 共生矩阵 相似度算法 数据编辑器 分析模块 相似程度 研究人员
• 年，卷（期）: 2015,（2）
• 页码范围: 11-24
• 页数: 14页
• 分类号: TP319

摘要

Purpose: To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions.Design/methodology/approach: We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS.Findings: When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity.Practical implications: We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results.Originality/value: This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS's hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.