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Within the area of association rules mining, previous algorithms, e.g., FP-Growth and Apriori, have been generally accepted with high appraisals respectively. Most of these algorithms decompose the problem of mining association rules into two subproblems: find frequent pattern and generate the desired rules. Therefore, such a decomposition strategy cannot but bring delay problem when the size of database...
We propose a method of association rule mining using genetic network programming (GNP) with time series processing mechanism and attribute accumulation mechanism in order to find time related sequence rules efficiently in association rule extraction systems. We suppose that, the database consists of a large number of attributes based on time series. In order to deal with databases which have a large...
In this paper, we present a method of comparative association rules mining using Genetic Network Programming (GNP) with attributes accumulation mechanism in order to uncover association rules between different datasets. GNP is an evolutionary approach which can evolve itself and find the optimal solutions. The motivation of the comparative association rules mining method is to use the data mining...
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