Self-optimization Rule-chain Mining Based on Potential Association Rule Directed Graph

被引：0
|
作者：
Ning Hong-yun [1 ,2 ]
Liu Jin-lan [1 ]
Zhang De-gan [2 ]
机构：
[1] Tianjin Univ, Sch Management, Tianjin 300072, Peoples R China
[2] Tianjin Univ, Sch Comp Sci & Technol, Tianjin 300191, Peoples R China
来源：
PROCEEDINGS OF THE 2008 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN, VOL 1 | 2008年
关键词：
D O I：
10.1109/ISCID.2008.17
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents an ACO-based (Ant Colony Optimization) mining algorithm aiming to discover longer rule-chains directly. Firstly, a potential association rule directed graph (PA Graph) is created, in which, the dynamic heuristics is used to record participant-intensity of edge. Secondly, making use of ant's positive feedback, pheromone on edge that ants passed is adjusted by heuristics so that it could make paths, which have longer rule-chains, have higher selection probability. Meanwhile, a bitwise-AND operation is introduced to compute rule's confidence easily. Finally, the experimental results show the proposed method can sufficiently capture longer rule-chains and it also confirms the robustness of the algorithm.
引用
收藏
页码：25 / +
页数：2
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