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Incremental association rule mining: a survey
Author(s) -
Nath B.,
Bhattacharyya D. K.,
Ghosh A.
Publication year - 2013
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1086
Subject(s) - association rule learning , data mining , k optimal pattern discovery , computer science , popularity , set (abstract data type) , process (computing) , association (psychology) , task (project management) , apriori algorithm , data science , machine learning , engineering , psychology , social psychology , philosophy , systems engineering , epistemology , programming language , operating system
Association rule mining is a computationally expensive task. Despite the huge processing cost, it has gained tremendous popularity due to the usefulness of association rules. Several efficient algorithms can be found in the literature. This paper provides a comprehensive survey on the state‐of‐the‐art algorithms for association rule mining, specially when the data sets used for rule mining are not static. Addition of new data to a data set may lead to additional rules or to the modification of existing rules. Finding the association rules from the whole data set may lead to significant waste of time if the process has started from the scratch. Several algorithms have been evolved to attend this important issue of the association rule mining problem. This paper analyzes some of them to tackle the incremental association rule mining problem. © 2013 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Association Rules Technologies > Classification