Reduction of Negative Rules in Association Rule Mining Using Distance Security and Genetic Algorithm

Girish Kumar Ameta

Abstract


The increasing rate of data is a challenging task for mined useful association rule in data mining. The classical association rule
mining generate rule with various problem such as pruning pass of transaction database, negative rule generation and superiority of rule set.
Time to time various researchers modified classical association rule mining with different approach. But in current scenario association rule
mining suffered from superiority rule generation. The problem of superiority is solved by multi-objective association rule mining, but this
process suffered continuity of rule generation. In this paper we are proposing a new algorithm distance weight optimization of association
rule mining. In this method, we find the near distance of rule set that uses equalize distance formula and generate two class higher class and
lower class .The validation of class checked by distance weight vector. Basically distance weight vector maintain a threshold value of rule
item sets. In whole process, we used genetic algorithm for optimization of rule set. Here we set population size is 1000 and selection
process validate by distance weight vector.

 

Keywords: Data mining, distance weight optimization, negative association rule mining,


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DOI: https://doi.org/10.26483/ijarcs.v4i3.1545

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