基于数据挖掘算法的通信网络多源数据处理与分类  

Multi-source Data Processing and Classification of Communication Network Based on Data Mining Algorithm

作　　者：洪彦 HONG Yan(Zhangzhou Vocational and Technical College,Zhangzhou 363000,Fujian Province,China)

机构地区：[1]漳州职业技术学院,福建漳州363000

出　　处：《景德镇学院学报》2022年第6期19-22,共4页Journal of JingDeZhen University

基　　金：漳州市自然科学项目(ZZ2019J18)。

摘　　要：通信网络多源数据冗余程度高、传输信息量大,而通过改进Apriori算法挖掘通信网络多源异常数据的关联规则特征,可以实现多源数据的正确分类。本文基于全局信息融合策略对原始采集的通信网络多源数据实施预处理,降低通信网络信息冗余;引入布尔矩阵辅助生成Apriori算法的候选项集与频繁项集,获得通信网络多源异常数据的关联规则,基于该关联规则构建分类器,实现通信网络多源数据分类。The communication network multi-source data has high redundancy and large amount of information transmission.The correct classification of multi-source data can be achieved by improving the Apriori algorithm to mine the association rule characteristics of the communication network multi-source abnormal data.Based on the global information fusion strategy, this paper preprocesses the original collected multi-source data of the communication network to reduce the information redundancy of the communication network;Boolean matrix is introduced to help generate candidate item sets and frequent item sets of Apriori algorithm to obtain association rules of multi-source abnormal data in communication network.Based on the association rules, a classifier is constructed to classify multi-source data in communication network.

关 键 词：数据挖掘 APRIORI算法 多源 关联规则 布尔矩阵 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]