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Analysis of Organizational Structure through Cluster Validation Techniques Evaluation of email communications at an organizational level
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik. Blekinge Inst Technol, Comp Sci & Engn Dept, Karlskrona, Sweden..
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik. Blekinge Inst Technol, Comp Sci & Engn Dept, Karlskrona, Sweden..
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
Telenor , SWE.
2017 (engelsk)Inngår i: 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, s. 170-176Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE , 2017. s. 170-176
Serie
International Conference on Data Mining Workshops, ISSN 2375-9232
Emneord [en]
cluster validation measures, data analysis, human capital management, internal communication, organizational structure
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-15992DOI: 10.1109/ICDMW.2017.28ISI: 000425845700022ISBN: 978-1-5386-3800-2 (tryckt)OAI: oai:DiVA.org:bth-15992DiVA, id: diva2:1192700
Konferanse
17th IEEE International Conference on Data Mining (ICDMW), NOV 18-21, 2017, New Orleans, LA
Tilgjengelig fra: 2018-03-23 Laget: 2018-03-23 Sist oppdatert: 2018-03-23bibliografisk kontrollert

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