Exploring attributes with domain knowledge in formal concept analysis

被引：15
作者：
机构：
[1] School of Computing Science and Engineering, VIT University, Vellore
[2] School of Information Technology, VIT University, Vellore
来源：
Annapurna, J. (jannapurna@gmail.com) | 1600年 / University of Zagreb Faculty of Electrical Engineering and Computing卷 / 21期
关键词：
Association rules; Attribute exploration; Background knowledge; Concept lattice; Formal concept analysis;
D O I：
10.2498/cit.1002114
中图分类号：
学科分类号：
摘要：
Recent literature reports the growing interests in data analysis using FormalConceptAnalysis (FCA), inwhich 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.
引用
收藏
页码：109 / 123
页数：14
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