scholarly journals Identifying Linear Models in Multi-Resolution Population Data Using Minimum Description Length Principle to Predict Household Income

2021 ◽  
Vol 15 (2) ◽  
pp. 1-30
Author(s):  
Chainarong Amornbunchornvej ◽  
Navaporn Surasvadi ◽  
Anon Plangprasopchok ◽  
Suttipong Thajchayapong
Keyword(s):  
Household Income ◽  
Linear Models ◽  
Minimum Description Length ◽  
Population Data ◽  
Minimum Description Length Principle

scholarly journals Substructure Discovery Using Minimum Description Length and Background Knowledge

1994 ◽  
Vol 1 ◽  
pp. 231-255 ◽  
Author(s):  
D. J. Cook ◽  
L. B. Holder
Keyword(s):  
Minimum Description Length ◽  
Background Knowledge ◽  
Structural Data ◽  
Original Data ◽  
Minimum Description Length Principle ◽  
Online Appendix ◽  
Discovery System ◽  
Graph Match ◽  
Domain Description ◽  
Graph Form

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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