Incremental association rule mining: a survey

Association rule mining is a computationally expensive task. Despite the huge processing cost, it is getting tremendous popularity due to the usefulness of the association rules. Several efficient algorithms can be found in the literature that cope with this popular task. This paper provides a comprehensive survey on the state-of-art algorithms for association rule mining, specially when the datasets used for rule mining are not static. Addition of new data to a dataset may lead to additional rules or to modification of existing rules. To find the association rules from the whole (old as well as new additional) dataset will be wastage of time only if the process is restarted 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. Copyright c © 0000 John Wiley & Sons, Ltd.