Outliers in evidential C-means: an empirical exploration using survey data on organizational social capital

Beynon, Malcolm James ORCID: https://orcid.org/0000-0002-5757-270X and Andrews, Rhys William ORCID: https://orcid.org/0000-0003-1904-9819 2014. Outliers in evidential C-means: an empirical exploration using survey data on organizational social capital. Presented at: BELIEF 2014: 3rd International Conference on Belief Functions, Oxford, UK, 26-28 September 2014. Published in: Cuzzolin, Fabio ed. Belief Functions: Theory and Applications: Third International Conference, BELIEF 2014, Oxford, UK, September 26-28, 2014. Proceeding. Lecture Notes in Computer Science. Lecture Notes in Computer Science , vol.8764 Springer, pp. 247-255. 10.1007/978-3-319-11191-9_27

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Abstract

Evidential C-Means (ECM) is a technique for cluster analysis, which has a methodology based on the Dempster-Shafer theory of evidence (DST). To date this technique has been theoretically discussed but has had limited application. Based on DST, ECM facilitates the association of objects to sets of clusters, rather than simply a single cluster. One feature of ECM is the facility for classifying cases to no cluster, the level of which is effected by the parameters in ECM (in particular (, which controls for the datapoints considered outliers). In this study, the substantive effects of varying ( are explored by investigating the relationship between organziational social capital and employee engagement. Drawing on a large-N survey of senior public sector executives, the clustering of different dimensions of organizational social capital is undertaken, and the relationship between those clusters and employee engagement analysed at varying levels of δ.The implications of the findings are discussed.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Subjects:	H Social Sciences > HB Economic Theory Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Springer
ISBN:	9783319111902
ISSN:	0302-9743
Date of First Compliant Deposit:	30 March 2016
Last Modified:	29 Nov 2024 18:12
URI:	https://orca.cardiff.ac.uk/id/eprint/65722

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