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Analysis of Organizational Structure through Cluster Validation Techniques Evaluation of email communications at an organizational level
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Inst Technol, Comp Sci & Engn Dept, Karlskrona, Sweden..ORCID iD: 0000-0003-3128-191x
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Inst Technol, Comp Sci & Engn Dept, Karlskrona, Sweden..
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Telenor , SWE.
2017 (English)In: 2017 17TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2017) / [ed] Gottumukkala, R Ning, X Dong, G Raghavan, V Aluru, S Karypis, G Miele, L Wu, X, IEEE , 2017, p. 170-176Conference paper, Published paper (Refereed)
Abstract [en]

In this work, we report an ongoing study that aims to apply cluster validation measures for analyzing email communications at an organizational level of a company. This analysis can be used to evaluate the company structure and to produce further recommendations for structural improvements. Our initial evaluations, based on data in the forms of emails logs and organizational structure for a large European telecommunication company, show that cluster validation techniques can be useful tools for assessing the organizational structure using objective analysis of internal email communications, and for simulating and studying different reorganization scenarios.

Place, publisher, year, edition, pages
IEEE , 2017. p. 170-176
Series
International Conference on Data Mining Workshops, ISSN 2375-9232
Keywords [en]
cluster validation measures, data analysis, human capital management, internal communication, organizational structure
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-15992DOI: 10.1109/ICDMW.2017.28ISI: 000425845700022ISBN: 978-1-5386-3800-2 (print)OAI: oai:DiVA.org:bth-15992DiVA, id: diva2:1192700
Conference
17th IEEE International Conference on Data Mining (ICDMW), NOV 18-21, 2017, New Orleans, LA
Part of project
Bigdata@BTH- Scalable resource-efficient systems for big data analytics, Knowledge FoundationAvailable from: 2018-03-23 Created: 2018-03-23 Last updated: 2021-07-25Bibliographically approved

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Boeva, VeselkaLundberg, Lars

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