期刊名称：Journal of Computing and Information Technology
印刷版ISSN：1330-1136
电子版ISSN：1846-3908
出版年度：2013
卷号：21
期号：2
页码：109-123
DOI：10.2498/cit.1002114
语种：English
出版社：SRCE - Sveučilišni računski centar
摘要：Recent literature reports the growing interests in data analysis using FormalConceptAnalysis (FCA), in which data is represented in the form of object and attribute relations. FCA analyzes and then subsequently visualizes the data based on duality called Galois connection. Attribute exploration is a knowledge acquisition process in FCA, which interactively determines the implications holding between the attributes. The objective of this paper is to demonstrate the attribute exploration to understand the dependencies among the attributes in the data. While performing this process, we add domain experts’ knowledge as background knowledge. We demonstrate the method through experiments on two real world healthcare datasets. The results show that the knowledge acquired through exploration process coupled with domain expert knowledge has better classification accuracy.
