A More General Form of Apriori and Its Application in Clustering Security Events

Jianxin Wang, Geng Zhao, Yunqing Xia
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Abstract

Due to its excellent performance, Apriori is frequently adopted to discover frequent itemsets, from which strong association rules can be easily generated, from among massive amounts of transactional or relational data. In this paper, Apriori is reconsidered with a more abstract perspective of data space, and a more general form of the algorithm is proposed. As is shown in this paper, the newly proposed form of the algorithm can be effectively applied to more situations in which its primitive form does not work. The more general form of Apriori is successfully applied to the problem of clustering security events organized in a hierarchical manner, which illustrates its usefulness.
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一种更一般形式的Apriori及其在安全事件聚类中的应用
由于其优异的性能,Apriori经常被用于从大量的事务或关系数据中发现频繁项集,由此可以容易地生成强关联规则。本文从更抽象的数据空间角度对Apriori进行了重新思考,并提出了一种更一般的算法形式。如本文所示,新提出的算法形式可以有效地应用于其原始形式不起作用的更多情况。Apriori的更一般形式成功地应用于以分层方式组织的安全事件的聚类问题,这说明了它的有用性。
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