基于Minkowski距离的一致聚类改进算法及应用研究  

Research on Improved Consensus Clustering Algorithm Based on Minkowski Distance and Its Application

作　　者：徐德刚[1] 徐戏阳 陈晓[1] 赵盼磊 苏志芳[1] 谢永芳[1] 阳春华[1] 

机构地区：[1]中南大学信息科学与工程学院,湖南长沙410083

出　　处：《湖南大学学报（自然科学版）》2016年第4期133-140,共8页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(614733319;61104135;61134006);国家创新研究群体科学基金资助项目(61321003);中南大学创新驱动计划(2016CX014)~~

摘　　要：针对一致聚类算法中聚类数目判断不准确、聚类速度慢等问题,通过集成复杂网络中的Newman贪婪算法与谱聚类算法,提出了一种新的基于Minkowski距离的一致聚类算法.该算法利用Minkowski距离刻画样本间的相似度,根据随机游走策略,结合不同数据的特征值分布分析方法进行聚类,实现聚类数目的自动识别.实验仿真说明算法具有较少的运算时间及较高的聚类精度.结合实际铜矿泡沫浮选过程特点,将该算法应用于浮选工况分类,进一步验证了算法的有效性.Aiming at the inaccuracy of clustering numbers and the slow speed of ordinary consensus clustering algorithms,Newman greedy algorithms of complex networks theory and spectral clustering algorithms were combined to propose a novel consensus clustering algorithm based on Minkowski distance.The algorithm depicts the similarity between samples in terms of Minkowski distance and adopts the strategy of random walk.By adjusting the parameters of the Laplacian distance,the accurate information of the clustering number is automatically obtained.The simulation results show that the proposed consensus clustering algorithm based on Minkowski distance has the superiority of the running time and accuracy of the clustering number.This method was applied to actual copper froth flotation process,and the results further illustrated its effectiveness.

关 键 词：一致聚类 Minkowski距离 一致矩阵 聚类数目 工况识别 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]