An incremental concept formation approach to learn and discover from a clinical database

V. Soo, Jan-Sing Wang, Shih-Pu Wang
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Abstract

The main interest of this research is to discover clinical implications from a large PTCA (Percutaneous Transluminal Coronary Angioplasty) database. A case-based concept formation model D-UNIMEM, modified from Lebowitz's UNIMEM, is proposed for this purpose. In this model, we integrated two kinds of class membership and the index-conjunction class membership. The former is a polythetic clustering approach that serves at the early stage of concept formation. The latter that allows only relevant instances to be placed in the same cluster serves as the later stage of concept formation. D-UNIMEM could extract interesting correlation among features from the learned concept hierarchy.<>
从临床数据库中学习和发现的增量概念形成方法
本研究的主要目的是从一个大的PTCA(经皮腔内冠状动脉成形术)数据库中发现临床意义。为此,提出了一种基于案例的概念形成模型D-UNIMEM,该模型在Lebowitz的UNIMEM基础上进行了改进。在该模型中,我们集成了两种类隶属度和索引关联类隶属度。前者是一种综合聚类方法,服务于概念形成的早期阶段。后者只允许将相关的实例放在同一集群中,作为概念形成的后期阶段。D-UNIMEM可以从学习的概念层次中提取特征之间有趣的相关性
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