基于特征提取的网络热点事件挖掘算法

Network Hot Event Mining Algorithm Based on Feature Extraction

为有效从网络中挖掘出民众关注的热点事件和话题,提高数据分类能力、热点追踪和检测正确率,在分析目前采用非结构化传统挖掘算法所存在问题的基础上,提出一种基于结构化分割的挖掘算法。首先通过分析热点事件挖掘处理流程,设计一种对热点事件数据挖掘的半结构化特征提取算法,对半结构化数据进行特征分割,生成大量请求,进而得到热点事件数据的分配因子,从而提高挖掘性能。仿真结果表明,该算法运行效率较高,精度较好,具有较高的稳健性。

For effectively mining the hot issues and topics concerned by people in network , improving the capabilities of data classification and the correct rate of hot tracking and detection , basing on analyzing the problems existing in the traditional un-structured mining algorithms used currently , we proposed a mining algorithm based on structured segmentation .First, by analy-zing the hot events mining process , we designed a semi-structured features extraction algorithm for hot events data mining , in or-der to make feature segmentation for semi-structured data , generate a lot of requests , and then get hot event data allocation fac-tors, thereby improve mining properties .Simulation results show that the algorithm is running with high efficiency , good accuracy and high robustness .