{"title":"聚类分析技术及其在图书馆情报学研究中的应用综述:k-means和k- medioids聚类","authors":"B. Lund, Jinxuan Ma","doi":"10.1108/pmm-05-2021-0026","DOIUrl":null,"url":null,"abstract":"PurposeThis literature review explores the definitions and characteristics of cluster analysis, a machine-learning technique that is frequently implemented to identify groupings in big datasets and its applicability to library and information science (LIS) research. This overview is intended for researchers who are interested in expanding their data analysis repertory to include cluster analysis, rather than for existing experts in this area.Design/methodology/approachA review of LIS articles included in the Library and Information Source (EBSCO) database that employ cluster analysis is performed. An overview of cluster analysis in general (how it works from a statistical standpoint, and how it can be performed by researchers), the most popular cluster analysis techniques and the uses of cluster analysis in LIS is presented.FindingsThe number of LIS studies that employ a cluster analytic approach has grown from about 5 per year in the early 2000s to an average of 35 studies per year in the mid- and late-2010s. The journal Scientometrics has the most articles published within LIS that use cluster analysis (102 studies). Scientometrics is the most common subject area to employ a cluster analytic approach (152 studies). The findings of this review indicate that cluster analysis could make LIS research more accessible by providing an innovative and insightful process of knowledge discovery.Originality/valueThis review is the first to present cluster analysis as an accessible data analysis approach, specifically from an LIS perspective.","PeriodicalId":44583,"journal":{"name":"Performance Measurement and Metrics","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"A review of cluster analysis techniques and their uses in library and information science research: k-means and k-medoids clustering\",\"authors\":\"B. Lund, Jinxuan Ma\",\"doi\":\"10.1108/pmm-05-2021-0026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PurposeThis literature review explores the definitions and characteristics of cluster analysis, a machine-learning technique that is frequently implemented to identify groupings in big datasets and its applicability to library and information science (LIS) research. This overview is intended for researchers who are interested in expanding their data analysis repertory to include cluster analysis, rather than for existing experts in this area.Design/methodology/approachA review of LIS articles included in the Library and Information Source (EBSCO) database that employ cluster analysis is performed. An overview of cluster analysis in general (how it works from a statistical standpoint, and how it can be performed by researchers), the most popular cluster analysis techniques and the uses of cluster analysis in LIS is presented.FindingsThe number of LIS studies that employ a cluster analytic approach has grown from about 5 per year in the early 2000s to an average of 35 studies per year in the mid- and late-2010s. The journal Scientometrics has the most articles published within LIS that use cluster analysis (102 studies). Scientometrics is the most common subject area to employ a cluster analytic approach (152 studies). The findings of this review indicate that cluster analysis could make LIS research more accessible by providing an innovative and insightful process of knowledge discovery.Originality/valueThis review is the first to present cluster analysis as an accessible data analysis approach, specifically from an LIS perspective.\",\"PeriodicalId\":44583,\"journal\":{\"name\":\"Performance Measurement and Metrics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2021-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Performance Measurement and Metrics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/pmm-05-2021-0026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Performance Measurement and Metrics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/pmm-05-2021-0026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
A review of cluster analysis techniques and their uses in library and information science research: k-means and k-medoids clustering
PurposeThis literature review explores the definitions and characteristics of cluster analysis, a machine-learning technique that is frequently implemented to identify groupings in big datasets and its applicability to library and information science (LIS) research. This overview is intended for researchers who are interested in expanding their data analysis repertory to include cluster analysis, rather than for existing experts in this area.Design/methodology/approachA review of LIS articles included in the Library and Information Source (EBSCO) database that employ cluster analysis is performed. An overview of cluster analysis in general (how it works from a statistical standpoint, and how it can be performed by researchers), the most popular cluster analysis techniques and the uses of cluster analysis in LIS is presented.FindingsThe number of LIS studies that employ a cluster analytic approach has grown from about 5 per year in the early 2000s to an average of 35 studies per year in the mid- and late-2010s. The journal Scientometrics has the most articles published within LIS that use cluster analysis (102 studies). Scientometrics is the most common subject area to employ a cluster analytic approach (152 studies). The findings of this review indicate that cluster analysis could make LIS research more accessible by providing an innovative and insightful process of knowledge discovery.Originality/valueThis review is the first to present cluster analysis as an accessible data analysis approach, specifically from an LIS perspective.
期刊介绍:
■Quantitative and qualitative analysis ■Benchmarking ■The measurement and role of information in enhancing organizational effectiveness ■Quality techniques and quality improvement ■Training and education ■Methods for performance measurement and metrics ■Standard assessment tools ■Using emerging technologies ■Setting standards or service quality