Mining fuzzy association rules

Keith C. C. Chan, Wai-Ho Au
1997 Proceedings of the sixth international conference on Information and knowledge management - CIKM '97  
In his paper, we introduce a novel technique, called F-APACS, for mining jkzy association rules. &istlng algorithms involve discretizing the domains of quantitative attrilmtes into intervals so as to discover quantitative association rules. i%ese intervals may not be concise and meaning@ enough for human experts to easily obtain nontrivial knowledge from those rules discovered. Instead of using intervals, F-APACS employs linguistic terms to represent the revealed regularities and exceptions.
more » ... linguistic representation is especially usefil when those rules discovered are presented to human experts for examination. The definition of linguistic terms is based on-set theory and hence we call the rides having these terms fuzzy association rules. The use of fq techniques makes F-APACS resilient to noises such as inaccuracies in physical measurements of real-life entities and missing values in the databases. Furthermore, F-APACS employs adjusted difference analysis which has the advantage that it does not require any user-supplied thresholds which are often hard to determine. The fact that F-APACS is able to mine fiuy association rules which utilize linguistic representation and that it uses an objective yet meanhg@ confidence measure to determine the interestingness of a rule makes it vety effective at the discovery of rules from a real-life transactional database of a PBX system provided by a telecommunication corporation
doi:10.1145/266714.266898 dblp:conf/cikm/ChanA97 fatcat:qcc6yp4m3ffelhiordvjvyaac4