@INPROCEEDINGS { Rsds3825,
author = {Busse-Grzymala, Jerzy W. and Than, Soe}, 
title = {An Algorithm to Compress Data in Learning from Examples}, 
booktitle = {Soft Computing}, 
conference = {International Workshop on Rough Sets and Soft Computing}, 
pages = {259--262}, 
publisher = {The Society for Computer Simulation}, 
address = {San Diego, U.S.A.}, 
month = {November}, 
year = 1994, 
editor = {Lin, Tsau Young and Wildberger, A.M.}, 
isbn = {1-56555-077-3}, 
abstract = {The input data for machine learning from examples are usually presented in a decision table. In such a table, examples are described by values of variables: attributes and a decision. A compression technique, discussed in this paper, is based on grouping together examples, attributes, and attribute-values into block such that blocks of examples and blocks of attributes are transformed into blocks of attribute-values the same way their elements are transformed in the original decision table. The original decision table is compressed into a smaller decision table while preserving the original structure. Thus it is much easier to induce rules from the compressed table, yet these rules still represent the essential knowledge. A heuristic algorithm for computing compressed decision tables is discussed.}, 
keywords = {machine learning, data compression, partition triples, decision tables, rough set theory (RST), }, 
}