| 加权增量关联规则挖掘在通信告警预测中的应用 |
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| 引用本文： | 王帅,杨秋辉,曾嘉彦,万莹,樊哲宁,张光兰. 加权增量关联规则挖掘在通信告警预测中的应用[J]. 计算机应用, 2018, 38(10): 2875-2880. DOI: 10.11772/j.issn.1001-9081.2018020392 |
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| 作者姓名： | 王帅 杨秋辉 曾嘉彦 万莹 樊哲宁 张光兰 |
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| 作者单位： | 四川大学 计算机学院, 成都 610065 |
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| 摘 要： | 针对通信网络告警预测中预测精度不高、模型训练效率较低等缺陷，提出告警权值确定方法和基于自然序树（Can-tree）的加权增量关联规则挖掘的通信网络告警预测方案。首先，对告警数据进行预处理，确定告警数据权值并压缩到Can-tree结构中；其次，应用增量关联规则挖掘算法对Can-tree进行挖掘，生成告警关联规则；最后，使用模式匹配的方法对实时告警信息进行预测，并对结果进行优化整理。实验结果表明，基于Can-tree的加权增量关联规则挖掘算法是高效的，利用前次挖掘的结果和信息提高了挖掘的效率，网络告警数据的权值分配方案能够合理地区分告警数据的重要程度，有助于将重要程度高的告警关联规则挖掘出来，并加快过时告警关联规则的淘汰，提高预测的准确度和精度。
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| 关 键 词： | 告警预测 通信网络 增量数据挖掘 加权关联规则挖掘 Can-tree算法 |
| 收稿时间： | 2018-02-26 |
| 修稿时间： | 2018-04-23 |
Application of weighted incremental association rule mining in communication alarm prediction |
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| WANG Shuai,YANG Qiuhui,ZENG Jiayan,WAN Ying,FAN Zhening,ZHANG Guanglan. Application of weighted incremental association rule mining in communication alarm prediction[J]. Journal of Computer Applications, 2018, 38(10): 2875-2880. DOI: 10.11772/j.issn.1001-9081.2018020392 |
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| Authors: | WANG Shuai YANG Qiuhui ZENG Jiayan WAN Ying FAN Zhening ZHANG Guanglan |
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| Affiliation: | College of Computer Science, Sichuan University, Chengdu Sichuan 610065, China |
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| Abstract: | Aiming at the shortcomings such as low prediction accuracy and low efficiency of model training in alarm prediction of communication networks, a communication network alarm forecasting scheme based on Canonical-order tree (Can-tree) weighted incremental association rule mining algorithm was proposed. Firstly, the alarm data was preprocessed to determine the alarm data weight and compressed into the Can-tree structure. Secondly, the Can-tree was mined by using the incremental association rule mining algorithm to generate alarm association rules. Finally, a pattern matching method was used to predict real-time alarm information, and the results were optimized. The experimental results show that the proposed method is efficient, and the previously mined results can improve the mining efficiency. The alarm weight assigning scheme can reasonably distinguish the importance of alarm data, help mine the alarm association rules with high importance, speed up the elimination of outdated alarm association rules, and improve the accuracy and precision of the prediction. |
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| Keywords: | alarm prediction communication network incremental data mining weighted association rule mining Can-tree algorithm |
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