Paper Info
Title
Rule generation in Lipski's incomplete information databases
Abstract
Non-deterministic Information Systems (NISs) are well known as systems for handling information incompleteness in data. In our previous work, we have proposed NIS-Apriori algorithm aimed at extraction of decision rules from NISs. NIS-Apriori employs the minimum and the maximum supports for each descriptor, and it effectively calculates the criterion values for defining rules. In this paper, we focus on Lipski's Incomplete Information Databases (IIDs), which handle non-deterministic information by means of the sets of values and intervals. We clarify how to understand decision rules in IIDs and appropriately adapt our NIS-Apriori algorithm to generate them. Rule generation in IIDs turns out to be more flexible than in NISs.
Year
DOI
Venue
2010
10.1007/978-3-642-13529-3_40
RSCTC
Keywords
Field
DocType
defining rule,decision rule,rule generation,information incompleteness,incomplete information databases,nis-apriori algorithm,non-deterministic information,criterion value,previous work,non-deterministic information systems,information system,incomplete information,rough set
Decision rule,Information system,Data mining,Apriori algorithm,Rough set,Artificial intelligence,Machine learning,Database,Mathematics,Complete information
Conference
Volume
ISSN
ISBN
6086
0302-9743
3-642-13528-5
Citations 
PageRank 
References 
7
0.51
12
Authors
3
Name
Order
Citations
PageRank
Hiroshi Sakai110716.41
Michinori Nakata229237.49
Dominik Ślęzak355350.04