基于人工智能技术的局部离群数据挖掘方法

Local outlier data mining based on artificial intelligence technology

针对传统离散数据挖掘方法存在内存消耗过大的问题,研究基于人工智能技术的局部离群数据挖掘方法。提取离散数据特征,并使用基于信息熵的算法检测局部离群数据。标准化处理检测出的数据,在神经网络中实现对局部离群数据的挖掘,完成对基于人工智能技术的局部离群数据挖掘方法的研究。通过与传统数据挖掘方法的对比实验结果可知,本文方法在数据挖掘过程中内存消耗较少,与传统方法相比具有明显的优越性,充分验证了该方法的应用性和有效性。

In view of the huge memory consumption of traditional discrete data mining methods,this paper proposes a local outlier data mining method based on artificial intelligence technology. The feature of the discrete data is extracted and the local outlier data is detected by the algorithm based on information entropy. Through standardized processing of the detected data,the mining of local outlier data in neural network is realized,and the research on local outlier data mining method based on artificial intelligence technology is completed. Experimental results show that,compared with the traditional data mining method,the proposed method consumes less memory in the process of data mining and has obvious advantages,which fully verifies the applicability and effectiveness of this method.