遗传优化的谱聚类方法研究  

Research of spectral clustering based on genetic algorithm

作　　者：王会青[1] 陈俊杰[1] 郭凯[1] 

机构地区：[1]太原理工大学计算机科学与技术学院,太原030024

出　　处：《计算机工程与应用》2011年第14期143-145,共3页Computer Engineering and Applications

基　　金：国家自然科学基金 No.60970059;山西省自然科学基金(No.2008011040)~~

摘　　要：传统的谱聚类对初始化数据敏感,聚类结果随不同的初始输入数据而波动。针对上述问题,提出了一种基于遗传算法的谱聚类算法,该算法克服了谱聚类算法对初始数据的敏感性,得到较稳定的聚类结果。与遗传k均值和谱聚类算法相比,该算法在模拟数据和UCI数据集上获得了较好的聚类性能。Spectral clustering algorithms are dependent on the initialization of the data,the clustering results are different when input data are not identical.To solve the problem,a spectral clustering based on genetic algorithm（GASC） is proposed, which overcomes the sensitivity of the initial data and get the more stable clustering result.Compared with the improved k-means algorithm and spectral clustering,the experiments show that the suggested algorithm has better clustering perfor- mance on both artificial and UCI data

关 键 词：谱聚类 遗传算法 谱图理论 K均值算法 机器学习 

分 类 号：TP311.12[自动化与计算机技术—计算机软件与理论]