Weighted Association Rule Mining - a Review

Traditional association rule mining consider support confident measures to find out frequent item sets, it assumes all items are having equal significance. Where as weighted association rule mining assigns weights to items based on different aspects. Because researchers are more concerned with qualitative aspects of attributes (e.g. significance), as compared to considering only quantitative ones (e.g. number of appearances in a database etc). Because qualitative properties are required in order to fully exploit the attributes present in the dataset. In last few years a number of weighted associative rule mining algorithms have been proposed, i.e. WAR, WARM, WFIM, WIP, WUARM, FWARM and others. These algorithms employ different rule discovery, rule ranking, rule pruning, rule prediction methods. This paper focuses on surveying and comparing the weighted associative rule mining techniques with regards to the above criteria.