基于近邻传播的不确定数据流演化聚类算法  

Affinity Propagation Based Evolutionary Clustering Algorithm for Uncertain Data Stream

作　　者：夏聪[1] 陆亿红[1] 

机构地区：[1]浙江工业大学计算机科学与技术学院,杭州310023

出　　处：《模式识别与人工智能》2016年第5期472-480,共9页Pattern Recognition and Artificial Intelligence

基　　金：水利部公益性行业科研专项(No.201401044);国家科技支撑计划项目(No.2012BAD10B01)资助~~

摘　　要：鉴于现有算法缺乏从时序演化角度解决不确定数据流聚类问题,提出基于近邻传播的不确定数据流演化聚类算法.考虑不确定数据流在线形成微簇时的变化因素对离线聚类的影响,提出不确定微簇变化率的概念.从不确定数据流演化的角度衡量微簇之间的相似程度,提出不确定微簇关联度的概念,并以此为基础构造不确定相似度矩阵,结合近邻传播思想实现不确定数据流演化聚类.通过实验证明文中算法的有效性和良好的聚类效果.The existing algorithms can not solve the clustering problems for uncertain data stream from the perspective of temporal evolution. An evolutionary clustering algorithm based on affinity propagation for uncertain data stream （EAP-UStream） is presented. A concept of change rate of uncertain micro-cluster is put forward with the consideration of the influence of the varying factors caused by the procedure of online uncertain data stream forming the micro-clusters on offline clustering. The degree of similarity between the micro-clusters is measured in terms of uncertain data stream evolution. A concept of coupling degree of uncertain micro-clusters is proposed. Thus, the uncertain similarity matrix is constructed, and evolutionary clustering for uncertain data stream is realized with the idea of affinity propagation. The experimental results show the effectiveness of EAP-UStream.

关 键 词：近邻传播 不确定数据流 演化聚类 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]