2009Unpublished venueRequires access

Mining Association Rules Based on Apriori Algorithm and Application

Pei‐ji Wang, Lin Shi, Jinniu Bai, Zhao Yu-lin

Open publisher page 36 citations

Abstract

In the data mining research, mining association rules is an important topic. Apriori algorithm submitted by Agrawal and R. Srikant in 1994 is the most effective algorithm. Aimed at two problems of discovering frequent itemsets in a large database and mining association rules from frequent itemsets, the author makes some research on mining frequent itemsets algorithm based on apriori algorithm and mining association rules algorithm based on improved measure system. Mining association rules algorithm based on support, confidence and interestingness is improved, aiming at creating interestingness useless rules and losing useful rules. Useless rules are cancelled, creating more reasonable association rules including negative items. The above method is used to mine association rules to the 2002 student score list of computer specialized field in Inner Mongolia university of science and technology.

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What this paper is about

In the data mining research, mining association rules is an important topic. Apriori algorithm submitted by Agrawal and R. Srikant in 1994 is the most effective algorithm. Aimed at two problems of discovering frequent itemsets in a large database and mining association rules from frequent itemsets, the author makes some research on mining frequent itemsets algorithm based on apriori algorithm and mining association rules algorithm based on improved measure system. Mining association rules algorithm based on support, confidence and interestingness is improved, aiming at creating interestingness useless rules and losing useful rules. Useless rules are cancelled, creating more reasonable association rules including negative items. The above method is used to mine association rules to the 2002 student score list of computer specialized field in Inner Mongolia university of science and technology.

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OpenAlex reports 36 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In the data mining research, mining association rules is an important topic. Apriori algorithm submitted by Agrawal and R. Srikant in 1994 is the most effective algorithm. Aimed at two problems of discovering frequent itemsets in a large database and mining association rules from frequent itemsets, the author makes some research on mining frequent itemsets algorithm based on apriori algorithm and mining association rules algorithm based on improved measure system. Mining association rules algorithm based on support, confidence and interestingness is improved, aiming at creating interestingness useless rules and losing useful rules. Useless rules are cancelled, creating more reasonable association rules including negative items. The above method is used to mine association rules to the 2002 student score list of computer specialized field in Inner Mongolia university of science and technology.

Key concepts: Association rule learning, Apriori algorithm, Data mining, Computer science, A priori and a posteriori, Field (mathematics), Association (psychology), Affinity analysis

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