Rough sets: a knowledge discovery technique for multifactorial medical outcomes.
Rough sets: a knowledge discovery technique for multifactorial medical outcomes.
复制标题
粗糙集：多因素医疗结果的知识发现技术。
DOI：
10.1097/00002060-200001000-00022
复制
发表时间：
2000
影响因子：
3
通讯作者：
Rowland,T
中科院分区：
文献类型：
--
作者：
Ohrn,A;Rowland,T
粗糙集是一种很有前途的数据挖掘和数据库知识发现技术。本文以非技术的方式介绍了粗糙集理论的基础知识，并概述了如何使用该技术从经验数据表中提取最小的if-then规则，这些表可以完全或近似地描述给定的示例分类。给出了脊髓损伤患者活动预测的应用实例。由于这些规则很容易解释，因此可以对它们进行检查，以产生关于各种促成因素如何相互作用的可能的新见解，从而作为进一步研究的假设生成器。此外，挖掘的规则集可以作为新的、未见过的情况的分类器。
Rough sets is a fairly new and promising technique for data mining and knowledge discovery from databases. This tutorial article presents the fundamentals of rough set theory in a nontechnical manner and outlines how the technique can be used to extract minimal if-then rules from tables of empirical data that either fully or approximately describe given example classifications. An example application for prediction of ambulation for patients with spinal cord injury is given. Because such rules are readily interpretable, they can be inspected to yield possible new insight into how various contributing factors interact and, thus, serve as hypothesis generators for further research. Additionally, the set of mined rules may function as a classifier of new, unseen cases.