Using clustering to extract personality information from socio economic data
It has become apparent that models that have been applied widely in economics, including Machine Learning techniques and Data Mining methods, should take into consideration principles that derive from the theories of Personality Psychology in order to discover more comprehensive knowledge regarding...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2012
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| Online Access: | https://eprints.nottingham.ac.uk/2075/ |
| _version_ | 1848790716587704320 |
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| author | Ladas, Alexandros Aickelin, Uwe Garibaldi, Jonathan M. Ferguson, Eamonn |
| author_facet | Ladas, Alexandros Aickelin, Uwe Garibaldi, Jonathan M. Ferguson, Eamonn |
| author_sort | Ladas, Alexandros |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | It has become apparent that models that have been applied widely in economics, including Machine Learning techniques and Data Mining methods, should take into consideration principles that derive from the theories of Personality Psychology in order to discover more comprehensive knowledge regarding complicated economic behaviours. In this work, we present a method to extract Behavioural Groups by using simple clustering techniques that can potentially reveal aspects of the Personalities for their members. We believe that this is very important because the psychological information regarding the Personalities of individuals is limited in real world applications and because it can become a useful tool in improving the traditional models of Knowledge Economy. |
| first_indexed | 2025-11-14T18:17:02Z |
| format | Conference or Workshop Item |
| id | nottingham-2075 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:17:02Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-20752020-05-04T20:22:34Z https://eprints.nottingham.ac.uk/2075/ Using clustering to extract personality information from socio economic data Ladas, Alexandros Aickelin, Uwe Garibaldi, Jonathan M. Ferguson, Eamonn It has become apparent that models that have been applied widely in economics, including Machine Learning techniques and Data Mining methods, should take into consideration principles that derive from the theories of Personality Psychology in order to discover more comprehensive knowledge regarding complicated economic behaviours. In this work, we present a method to extract Behavioural Groups by using simple clustering techniques that can potentially reveal aspects of the Personalities for their members. We believe that this is very important because the psychological information regarding the Personalities of individuals is limited in real world applications and because it can become a useful tool in improving the traditional models of Knowledge Economy. 2012 Conference or Workshop Item PeerReviewed Ladas, Alexandros, Aickelin, Uwe, Garibaldi, Jonathan M. and Ferguson, Eamonn (2012) Using clustering to extract personality information from socio economic data. In: 12th UK Workshop on Computational Intelligence (UKCI 2012), 5-7 Sept 2012, Edinburgh, Scotland. (Unpublished) |
| spellingShingle | Ladas, Alexandros Aickelin, Uwe Garibaldi, Jonathan M. Ferguson, Eamonn Using clustering to extract personality information from socio economic data |
| title | Using clustering to extract personality information from socio economic data |
| title_full | Using clustering to extract personality information from socio economic data |
| title_fullStr | Using clustering to extract personality information from socio economic data |
| title_full_unstemmed | Using clustering to extract personality information from socio economic data |
| title_short | Using clustering to extract personality information from socio economic data |
| title_sort | using clustering to extract personality information from socio economic data |
| url | https://eprints.nottingham.ac.uk/2075/ |