基于人工蜂群优化的数据流聚类算法  

Data Stream Clustering Algorithm Based on Artificial Bee Colony Optimization

作　　者：贾东立[1] 申飞 崔新宇 JIA Dong-Li;SHEN Fei;CUI Xin-Yu(School of Information and Electrical Engineering,Hebei University of Engineering,Handan 05600038,China)

机构地区：[1]河北工程大学信息与电气工程学院

出　　处：《计算机系统应用》2020年第2期145-150,共6页Computer Systems & Applications

基　　金：河北省高等学校科学技术研究项目(ZD2015087);邯郸市科学技术研究与发展计划(1721203049-1)~~

摘　　要：在传统分段式数据流聚类算法中,在线部分中的微簇阈值半径T取值不精确以及离线部分对微聚类的处理相对简单,导致了聚类质量不高.针对这一缺点,在现有动态滑动窗口模型基础上,提出了一种针对离线部分处理的基于人工蜂群优化的数据流聚类算法.该算法包括两部分:(1)在线部分根据数据在窗口内停留的时间长短来动态调整窗口的大小和改进微簇阈值半径T的取值,逐步得到微簇集.(2)离线部分利用改进的蜂群算法不断动态调整来求出最优聚类结果.实验结果证明,本文算法不但有较高的聚类质量,而且有较好的延展性和稳定性.In the traditional segmented data stream clustering algorithm,the inaccuracy of micro-cluster threshold radius T in the online part as well as the oversimplifying of the dealing process with the micro-cluster by the offline part leads to a low clustering quality.In order to break through such limitation,a data stream clustering algorithm on the basis of artificial bee colony optimization for offline part processing is proposed based on the existing dynamic sliding window model.This algorithm consists of two parts:(1)The online part dynamically adjusts the size of the window and improves the value of the micro-cluster threshold radius T according to the length of time that the data stays in the window so as to get micro clustering step by step.(2)The offline part uses the improved bee colony algorithm to continuously adjust dynamically to find the optimal clustering result.The experimental results show that this algorithm not only bears a high clustering quality,but also has fairly good ductility and stability.

关 键 词：数据流聚类 动态滑动窗口 人工蜂群算法 微簇阈值半径 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP18[自动化与计算机技术—计算机科学与技术]