用聚类方法从社会经济数据中提取个性信息

Alexandros Ladas, U. Aickelin, J. Garibaldi, E. Ferguson
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引用次数: 3

摘要

很明显,在经济学中广泛应用的模型,包括机器学习技术和数据挖掘方法,应该考虑来自人格心理学理论的原则,以便发现有关复杂经济行为的更全面的知识。在这项工作中,我们提出了一种方法,通过使用简单的聚类技术提取行为组,可以潜在地揭示其成员的个性方面。我们认为这是非常重要的,因为关于个人个性的心理信息在现实世界中的应用是有限的,因为它可以成为改进传统知识经济模型的有用工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.
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