The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS
ZHOU Qiuju1; LENG Fuhai1; LEYDESDORFF Loet2; Fuhai Leng (E-mail: lengfh@mail.las.ac.cn).
2015-06-27
发表期刊Chinese Journal of Library and Information Science
卷号8期号:2页码:11-24
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.; 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.
文章类型Research Papers
关键词Co-occurrence Matrices Hierarchical Cluster Analysis Spss Similarity Algorithm The Syntax Editor
学科领域新闻学与传播学 ; 图书馆、情报与文献学
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语种英语
文献类型期刊论文
条目标识符http://ir.las.ac.cn/handle/12502/7807
专题Journal of Data and Information Science_Chinese Journal of Library and Information Science-2015
通讯作者Fuhai Leng (E-mail: lengfh@mail.las.ac.cn).
作者单位1.National Science Library, Chinese Academy of Sciences, 100190 Beijing, China
2.University of Amsterdam, Amsterdam School of Communication Research (ASCoR), PO Box 15793, 1001 NG Amsterdam, the Netherlands
第一作者单位中国科学院文献情报中心
推荐引用方式
GB/T 7714
ZHOU Qiuju,LENG Fuhai,LEYDESDORFF Loet,et al. The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS[J]. Chinese Journal of Library and Information Science,2015,8(2):11-24.
APA ZHOU Qiuju,LENG Fuhai,LEYDESDORFF Loet,&Fuhai Leng .(2015).The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS.Chinese Journal of Library and Information Science,8(2),11-24.
MLA ZHOU Qiuju,et al."The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS".Chinese Journal of Library and Information Science 8.2(2015):11-24.
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