Paper Information

Journal:   JOURNAL OF INFORMATION TECHNOLOGY MANAGEMENT   SUMMER 2015 , Volume 7 , Number 2; Page(s) 259 To 282.
 
Paper: 

COMBINES THE APRIORI AND FCM ALGORITHM TO IMPROVE THE EXTRACTED ASSOCIATION RULES WITH DETERMINE THE MINIMUM SUPPORT AUTOMATICALLY

 
 
Author(s):  JAFARZADEH HEYDAR*, ASGARI CHAMRAN, AMIRY AMIR
 
* SCIENCE AND RESEARCH BRANCH, ISLAMIC AZAD UNIVERSITY, ILAM, IRAN
 
Abstract: 

Association rules mining is one of the most popular data mining models. Single minimum support are used in classic association rules mining algorithms, like Apriori, while new approaches tried to promote classic algorithms, like MSapriori, use multiple minimum support. In both cases, the user has to specify the minimum support. Let’s say the user wants to apply Apriori algorithm on a database with millions of transactions. They can’t possibly have all the necessary knowledge about all the transactions in the database and thus cannot specify the minimum support. In this paper, using fuzzificated data and averaging techniques, we propose a method in which Apriori algorithm would specify the minimum support in a fully automated manner. The simulation results on a real example show that our approach works better than the classic Apriori algorithm.

 
Keyword(s): APRIORI ALGORITHM, ASSOCIATION RULES, SUPPORT FUZZY CLUSTERING, FREQUENT PATTERNS
 
References: 
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