期刊
APPLIED ARTIFICIAL INTELLIGENCE
卷 26, 期 3, 页码 274-301出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/08839514.2012.648457
关键词
-
资金
- National Board of Higher Mathematics, Department of Atomic Energy, Government of India [2/48(11)/2010-RD II/10806]
Formal Concept Analysis (FCA), in which data is represented as a formal context, offers a framework for Association Rules Mining (ARM) by handling functional dependencies in the data. However, with the size of the formal context, the number of rules grows exponentially. In this article, we apply Fuzzy K-Means clustering on the data set to reduce the formal context and FCA on the reduced data set for mining association rules. With experiments on two real-world healthcare data sets, we offer the evidence for performance of FKM-based FCA in mining association rules.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据