Optimized Multi-Algorithm Voting: Increasing objectivity in clustering

DC FieldValueLanguage
dc.contributor.authorKempen, Regina
dc.contributor.authorMeier, Alexander
dc.contributor.authorHasche, Jens
dc.contributor.authorMueller, Karsten
dc.date.accessioned2021-12-23T16:05:42Z-
dc.date.available2021-12-23T16:05:42Z-
dc.date.issued2019
dc.identifier.issn09574174
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/7160-
dc.description.abstractCurrently, the influence of a single statistical cluster algorithm on the results of clustering procedures represents a major threat to the objectivity in clustering. To exemplify this question, this paper refers to country clustering in cross-cultural research. In this field, previous research has determined differing numbers of clusters, depending on choices available for the clustering procedure, leading to a high number of inconsistent results. Hence, it is argued that the variety in cluster solutions induced by the choice of different statistical cluster algorithms should be reduced. To this end, this study builds on Multi-Algorithm Voting (MAV) procedure introduced by Bittmann and Gelbard (2007) and presents an advancement to the MAV method. Specifically, MAV procedure is refined for the analysis of larger data sets using the simulated annealing algorithm for optimization. The use of this Optimized MAV (OMAV) is then demonstrated for country clustering in cross-cultural research. Specifically, a set of 57 countries is divided into 12 clusters based on work-related values obtained from GLOBE database reported in House et al. (2004). Thus, results clearly show that the objectivity of clustering results can be significantly improved based on OMAV. Implications for expert and intelligent systems on the use of OMAV are discussed. Namely, OMAV represents a powerful tool supporting the decision-making process in cluster analysis reducing the number of subjective and arbitrary decisions. Taken together, this study contributes to existing literature by providing an integrative and robust method of country clustering using OMAV and by presenting country clusters applicable to various settings. (C) 2018 Elsevier Ltd. All rights reserved.
dc.language.isoen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONS
dc.subjectClustering
dc.subjectComputer Science
dc.subjectComputer Science, Artificial Intelligence
dc.subjectCOUNTRIES
dc.subjectCULTURE
dc.subjectDIMENSIONS
dc.subjectEngineering
dc.subjectEngineering, Electrical & Electronic
dc.subjectFIT
dc.subjectIntegrative methods
dc.subjectMulti-algorithm voting
dc.subjectOperations Research & Management Science
dc.subjectPATTERNS
dc.subjectPERSONALITY-TRAITS
dc.subjectPROFILES
dc.subjectVALUES
dc.subjectWork-related values
dc.titleOptimized Multi-Algorithm Voting: Increasing objectivity in clustering
dc.typejournal article
dc.identifier.doi10.1016/j.eswa.2018.09.047
dc.identifier.isiISI:000451653400016
dc.description.volume118
dc.description.startpage217
dc.description.endpage230
dc.contributor.researcheridH-7418-2019
dc.identifier.eissn18736793
dc.publisher.placeTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
dcterms.isPartOf.abbreviationExpert Syst. Appl.
crisitem.author.deptFB 08 - Humanwissenschaften-
crisitem.author.deptidfb08-
crisitem.author.orcid0000-0001-7389-8024-
crisitem.author.parentorgUniversität Osnabrück-
crisitem.author.netidMuKa529-
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