@INPROCEEDINGS { Rsds2141,
author = {Lin, Tsau Young}, 
title = {Mining Un-interpreted Generalized Association Rules by Linear Inequalities. Descriptive/Deductive Data Mining Approach}, 
booktitle = {Rough Sets and Current Trends in Computing}, 
conference = {Rough Sets and Current Trends in Computing}, 
series =  = {Lecture Notes in Artificial Intelligence}, 
volume = 3066, 
pages = {204-212}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
month = {June}, 
year = 2004, 
editor = {Tsumoto, Shusaku and Slowinski, Roman W. and Komorowski, Jan and Grzymala-Busse, Jerzy W.}, 
issn = {0302-9743}, 
isbn = {3-540-22117-4}, 
abstract = {Taking the spirit of descriptive statistic methods data mining is viewed as a deductive science, no inductive generalizations or predicative assertions. We call this approach descriptive/deductive data mining (DDM) to stress spirit of descriptive statistic methods and the role of mathematical deductions. Such a seemingly restrictive methodology, somewhat surprisingly, turns out to be quite far reaching. Previously, we have observed in ICDM02 that (1) Isomorphic relations have isomorphic patterns (classical association rules). This observation implies, from data mining prospect, that relations and patterns are syntactic in nature. We also have reported that (2) attributes or features (including un-interpreted ones) of a given relation can be enumerated mathematically, though, in intractable time. In this paper, we proved (3) generalized association rules (including un-interpreted rules) can be discovered by solving a finite set of integral linear inequalities within polynomial time.}, 
keywords = {association rules, attribute, feature, bitmap indexes, granular data model, data mining, }, 
}