Traditional association rule mining consider support confident measures to find out frequent item sets, it assumes all items are having equal significance. Whereas 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.