scholarly journals On the minimum description length principle for sources with piecewise constant parameters

1993 ◽  
Vol 39 (6) ◽  
pp. 1962-1967 ◽  
Author(s):  
N. Merhav
Keyword(s):  
Minimum Description Length ◽  
Minimum Description Length Principle ◽  
Piecewise Constant

scholarly journals Clustgrams: an extension to histogram densities based on the minimum description length principle

2011 ◽  
Vol 1 (4) ◽  
Author(s):  
Panu Luosto ◽  
Petri Kontkanen
Keyword(s):  
Machine Learning ◽  
Density Estimation ◽  
Minimum Description Length ◽  
Complex Problem ◽  
Density Functions ◽  
Large Sample Size ◽  
Minimum Description Length Principle ◽  
Piecewise Constant ◽  
Problem Instances ◽  
Model Selection Problem

AbstractDensity estimation is one of the most important problems in statistical inference and machine learning. A common approach to the problem is to use histograms, i.e., piecewise constant densities. Histograms are flexible and can adapt to any density given enough bins. However, due to the simplicity of histograms, a large number of parameters and a large sample size might be needed for learning an accurate density, especially in more complex problem instances. In this paper, we extend the histogram density estimation framework by introducing a model called clustgram, which uses arbitrary density functions as components of the density rather than just uniform components. The new model is based on finding a clustering of the sample points and determining the type of the density function for each cluster. We regard the problem of learning clustgrams as a model selection problem and use the theoretically appealing minimum description length principle for solving the task.

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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