一种基于不确定数据的高效聚类算法  

An Efficient Clustering Algorithm Based on Uncertain Data

作　　者：李峰[1] LI Feng(College of Computer and Communication,Hunan Institute of Engineering,Xiangtan 411104,China)

机构地区：[1]湖南工程学院计算机与通信学院,湘潭411104

出　　处：《湖南工程学院学报（自然科学版）》2022年第4期55-59,共5页Journal of Hunan Institute of Engineering(Natural Science Edition)

摘　　要：基于不确定数据的聚类研究是数据挖掘领域的一个重要的研究分支,在计算机科学、数学、统计学、生物学和经济学等领域都有广泛的应用.本文提出一种使用分类器代替静态阈值,并采用边缘信息预测边缘邻域的聚类算法,构造2个独立的初始分类器以提高识别精度.实验结果表明,该方法能节省大量运行时间和空间,大幅提高挖掘效率.Clustering based on uncertain data is an important research branch in the field of data mining.It has a large number of applications in computer science,mathematics,statistics,biology and economics.An algorithm based on edge information is proposed to predict the neighborhood of edge and a classifier instead of static threshold is used to cluster.Then two independent initial classifiers are constructed to improve the recognition accuracy.Experimental results show that this method can save a lot of running time and space,and improve the mining efficiency greatly.

关 键 词：不确定数据 边缘信息 分类器 聚类 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]