Substructure Discovery Using Minimum Description Length and Background Knowledge

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. Description of Online Appendix: This is a compressed tar file containing the SUBDUE discovery system, written in C. The program accepts as input databases represented in graph form, and will output discovered substructures with their corresponding value.

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Discovering Concepts in Structural Data

Pattern Discovery in Biomolecular Data ◽

10.1093/oso/9780195119404.003.0016 ◽

1999 ◽

Author(s):

Diane J. Cook ◽

Lawrence B. Holder

Keyword(s):

Data Analysis ◽

Missing Data ◽

Data Acquisition ◽

Domain Knowledge ◽

Minimum Description Length ◽

Structural Data ◽

Original Data ◽

Graph Match ◽

Replacement Process ◽

Data Objects

The large amount of data collected today is quickly overwhelming researchers’ abilities to interpret the data and discover interesting patterns. In response to this problem, a number of researchers have developed techniques for discovering concepts in databases. These techniques work well for data expressed in a nonstructural, attribute-value representation and address issues of data relevance, missing data, noise and uncertainty, and utilization of domain knowledge (Fisher, 1987; Cheeseman and Stutz, 1996). However, recent data acquisition projects are collecting structural data describing the relationships among the data objects. Correspondingly, there exists a need for techniques to analyze and discover concepts in structural databases (Fayyad et al., 1996b). One method for discovering knowledge in structural data is the identification of common substructures. The goal is to find substructures capable of compressing the data and to identify conceptually interesting substructures that enhance the interpretation of the data. Substructure discovery is the process of identifying concepts describing interesting and repetitive substructures within structural data. Once discovered, the substructure concept can be used to simplify the data by replacing instances of the substructure with a pointer to the newly discovered concept. The discovered substructure concepts allow abstraction over detailed structure in the original data and provide new, relevant attributes for interpreting the data. Iteration of the substructure discovery and replacement process constructs a hierarchical description of the structural data in terms of the discovered substructures. This hierarchy provides varying levels of interpretation that can be accessed based on the goals of the data analysis. We describe a system called Subdue that discovers interesting substructures in structural data based on the minimum description length (MDL) principle. The Subdue system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previously discovered substructures, 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.

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