大数据下一种规则的快速挖掘技术研究
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摘要
近年来,数据挖掘技术已经应用到各个领域。数据挖掘通常会产生大量规则,产生的关联规则大多数是冗余的,导致用户难以分析并利用这些数据。本文致力于在大数据下对大量的冗余规则进行修剪,提出一种修剪算法的改进算法,并通过试验证明了该方法的有效性。
In recent years, data mining technology has been applied to various fields. Data mining usually generates a large number of rules, and the resulting association rules are mostly redundant, making it difficult for users to analyze and utilize the data. This paper was devoted to pruning a large number of redundant rules under big data, and gave an improved algorithm of pruning algorithm, and proved its effectiveness through experiments.
In recent years, data mining technology has been applied to various fields. Data mining usually generates a large number of rules, and the resulting association rules are mostly redundant, making it difficult for users to analyze and utilize the data. This paper was devoted to pruning a large number of redundant rules under big data, and gave an improved algorithm of pruning algorithm, and proved its effectiveness through experiments.
引文
[1]Agrawal R,Srikant R. Fast Algorithms for Mining Association Rules in Large Databases[C]//International Conference on Very Large Data Bases. Morgan Kaufmann Publishers Inc,1994.
[2]Srikant R,Vu Q,Agrawal R. Mining Association Rules with Item Constraints[C]//International Conference on Knowledge Discovery and Data Mining. AAAI Press,1997.
[3]Aggarwal C C,Yu P S. Online Generation of Association Rules[J]. Knowledge&Data Engineering IEEE Transactions on,2001(4):527-540.
[2]Srikant R,Vu Q,Agrawal R. Mining Association Rules with Item Constraints[C]//International Conference on Knowledge Discovery and Data Mining. AAAI Press,1997.
[3]Aggarwal C C,Yu P S. Online Generation of Association Rules[J]. Knowledge&Data Engineering IEEE Transactions on,2001(4):527-540.