Substructure Discovery Using Minimum Description Length and Background Knowledge
Substructure Discovery Using Minimum Description Length and Background Knowledge
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DOI：
10.1613/jair.43
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发表时间：
1993-01-01
影响因子：
5
通讯作者：
Holder, Lawrence B.
中科院分区：
文献类型：
--
作者：
Cook, Diane J.;Holder, Lawrence B.
识别有趣和重复的子结构的能力是在结构化数据中发现知识的重要组成部分。我们描述了一个新版本的Subdue子结构发现系统的最小描述长度原则的基础上。Subdue系统发现压缩原始数据并表示数据中的结构概念的子结构。通过替换数据中以前发现的子结构，Subdue的多次传递产生数据中结构化子结构的分层描述。Subdue使用计算有界的不精确图匹配来识别子结构的相似但不相同的实例，并在计算约束下找到两个子结构的接近度的近似度量。除了最小描述长度原则之外，Subdue还可以使用其他背景知识来指导搜索更合适的子结构。在不同领域的实验表明，Subdue能够找到能够压缩原始数据的子结构，并发现对该领域重要的结构概念
The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. We describe a new version of our Subdue substructure discovery system based on the minimum description length principle. The Subdue system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered substructures in the data, multiple passes of Subdue produce a hierarchical description of the structural regularities in the data. Subdue uses a computationally-bounded inexact graph match that identifies similar, but not identical, instances of a substructure and finds an approximate measure of closeness of two substructures when under computational constraints. In addition to the minimum description length principle, other background knowledge can be used by Subdue to guide the search towards more appropriate substructures. Experiments in a variety of domains demonstrate Subdue's ability to find substructures capable of compressing the original data and to discover structural concepts important to the domain