slugWeighted Association Rule Mining:A Survey
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 4
Abstract
Association rule mining helps to extract large transaction databases for association rule. Without taking the weight of items into account, Classical Association Rule Mining (ARM) concludes that all items have the same significance. It also avoids the difference between the importance and transactions of all itemsets. In converse, WARM (Weighted Association Rule Mining) doesn't work on databases with only binary attributes, but also makes the use of importance of all transactions and itemset. It needs every item to be given weight to reflect their importance to the user. The weights may correlate to the benefit of different items. A number of weighted associative rule mi ning algorithms have been introduced in last few years such as WAR , WARM, WFIM, WIP, FWA RM,WFP and many more. These algorithms engage different rule pruning, rule prediction, rule discovery, rule ranking methods. This paper targets on comparing and surveying the weighted associative rule mining techniques with regards to the above criteria.
Authors and Affiliations
Shashi Chhikara, Purushottam Sharma
Keywords
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- EP ID EP17835
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