基于可能性的关联规则挖掘算法

Zhi-Wei Xu, Xue-Feng Zhang, Haiwang Zhang
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引用次数: 0

摘要

在本文中,首先提出了这种趋势在服务框架上的可能性——一定程度的依赖。该框架与支持信心框架完全不同。D数据库是将事件的倾向性与事件的程度联系起来的程度,包含度量规则。相对而言,更直接地描述了事件之间的因果关系,更有效地解决了关联规则的冗余挖掘问题。在染色体中,使用一种新的因子编码方法。D.有效压缩了数据库的存储空间,提高了操作速度。从实验结果来看,采用遗传算法寻找值在效率和精度率规则上具有一定的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Based on the Possibility of an Association Rule Mining Algorithm
In this paper, first raised the possibility of such a tendency on Services Framework -- a degree of dependence. The framework and support-confidence framework completely different. D database is the tendency of the incident and the extent of the incidents linked to the degree of measure contains rules. Comparatively speaking, more direct causal relationship between the events described, a more effective solution to the problem of redundant Mining Association Rules. In chromosomes, using a novel factor-encoding methods. D. effective compression of the database storage space and improves the speed of operation. From experimental results, using a genetic algorithm to find value in the efficiency and precision rate rules have certain advantages.
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