1. Association rule mining using hbpso
Authors : Amit Kumar Chandanan, Dr Kavita, Dr M K Shukla
Pages : 37-44
DOI : http://dx.doi.org/10.21172/1.841.06
Keywords : Redundant Rules, Association Rule, HBPSO, Genetic Alorithm Abstract :Association rules are one of the most researched areas of data mining. In the area of data mining finding association between data set or product in marketing field plays important role. Association rule mining is a way to find relations or co-relations among a set of information available. The aim to generate rules for giving multiple data from various databases. Analysis of data can be possible with the help of sequential access of data from database. In case of sequential access of data, it may cause multiple times same rules to be generated. It is desired to find a solution to get out of those unnecessary association rules due to the complex characteristics of serial data. Although many numbers of serial association rule with the use of either sequence or temporal constraint as prediction model, these two models did not consider with the repetition during the process of rule mining for the database. Duplicate data set generates redundant association rules with respect to support and confidence. In proposed method, remove redundant rules to improve efficiency of association rules with HBPSO. The experimental result comparison shows the improvement in quality of association rules.
Citing this Journal Article :Amit Kumar Chandanan, Dr Kavita, Dr M K Shukla, "Association rule mining using hbpso", Volume 8 Issue 4-1 - August 2017, 37-44
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