Evaluation on distributed algorithmic performance of associated rule 关联规则分布式算法的性能评价
This paper provides a situation of the study in association rule generation, brings forward algorithmic of cycle association rule generation in assembles, analyses problems existing in the algorithmic and brings forward solutions and last views some study directions in future in a association rule generation. 介绍了关联规则挖掘的研究情况,提出了基于聚类的周期关联规则挖掘算法,分析了该算法存在的问题并提出解决方案,展望了关联规则挖掘的未来研究方向。
In order to improve algorithmic efficiency and precision, an association rule extracted from spacecraft telemetry data through data mining technology was applied to configure parameters of AHP. 利用数据挖掘技术从航天器遥测数据中提取关联规则,代替人类专家的经验知识自主配置AHP算法中的参数,以提高算法的效率和精度。
The core part is user behavior feature extraction module. The feature extraction algorithmic determines not only the precision of recommendation rule but also the quality of system and the satisfaction of users. 其中本子系统的核心部分为用户行为特征提取模块的设计和实现部分,特征提取算法的好坏直接决定了推荐规则产生的准确性高低,同时也影响了系统的性能的优劣和用户的满意度。
Secondly, this paper puts forward the algorithmic framework of association rule mining using Markov Blanket, and also explores each components of algorithmic framwork. 给出了一种利用马尔可夫覆盖进行关联规则挖掘的算法框架,并研究了算法中的各个组成部分。