Open AccessA study of frequent itemset mining techniques
Author(s) -
Sachin Sharma,
Shaveta Bhatia
Publication year - 2017
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v6i4.8300
Subject(s) - association rule learning , task (project management) , data mining , computer science , association (psychology) , set (abstract data type) , key (lock) , value (mathematics) , apriori algorithm , information retrieval , machine learning , engineering , computer security , philosophy , systems engineering , epistemology , programming language
Frequent item set is the most crucial and expensive task for the industry today. It is the task of mining the information from different sources and a key approach in Data Mining. Frequent item sets satisfying the minimum threshold can be discovered. Association rules are extracted from frequent item sets. The Association rules are affected by the minimum support value entered by the user may be considered as Positive or negative. There may be some other Association rules, which involve the rare item sets. Various methods have been used by researchers for generating the Association Rules. In this paper, our aim is to study various techniques to generate the Association rules.