[1]胡海峰[][],刘萍萍[]. 一种基于特征间隙的检测簇数的谱聚类算法[J].计算机技术与发展,2015,25(09):37-42.
　HU Hai-feng[][],LIU Ping-ping[]. A Spectral Clustering Algorithm with Identifying Clustering Number Based on Eigengap[J].,2015,25(09):37-42.
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 一种基于特征间隙的检测簇数的谱聚类算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年09期
页码:
37-42
栏目:
智能、算法、系统工程
出版日期:
2015-09-10

文章信息/Info

Title:
 A Spectral Clustering Algorithm with Identifying Clustering Number Based on Eigengap
文章编号:
1673-629X（2015)09-0037-06
作者:
 胡海峰[1][2];  刘萍萍[1]
1.南京邮电大学 通信与信息工程学院 宽带无线通信与传感网技术教育部重点实验室；2.东南大学 移动通信国家重点实验室
Author(s):
 HU Hai-feng[1][2];  LIU Ping-ping[1]
关键词:
 ; 谱聚类; 簇数; 特征间隙; 高斯相似度
Keywords:
 ; spectral clustering; clustering number; eigengap Gaussian similarity
分类号:
TP301.6
文献标志码:
A
摘要:
 数据挖掘中如何根据数据之间的相似度确定簇( Cluster)数一直是聚类算法中需要解决的难题。文中在经典谱聚( Spectral Clustering)算法的基础上提出了一种基于特征间隙检测簇数的谱聚类算法( Spectral Clustering with Identifying Clustering Number based on Eigengap,SC-ICNE)。通过构建规范的拉普拉斯矩阵,顺序求解其特征值和相应特征向量,并得到矩阵相邻特征值的间隙,通过判断特征间隙的位置来确定簇数k。最后,通过对前k个特征向量的k-means算法实现数据集的聚类。文中通过仿真分析了高斯相似度函数对SC-ICNE聚类性能的影响,在非凸球形数据集和UCI数据集上进行了性能仿真,并和k-means聚类算法进行了对比,在检测簇数和聚类准确性方面,验证了SC-ICNE算法的有效性。
Abstract:
 Choosing the number k of clusters based on the degree of correlation is a general problem for all clustering algorithms. Based on the classical spectral clustering algorithm,propose a Spectral Clustering with Identifying Clustering Number based on Eigengap ( SC-IC-NE) algorithm. The SC-ICNE algorithm computes eigenvalues and corresponding eigenvectors of normalized graph Laplacians sequen-tially. Furthermore,the number of cluster k can be identified via the eigengap between the adjacent eigenvalues. Finally,the data can be clustered using the first k eigenvectors with the k-means algorithm. In the simulation,the effect of the Gaussian similarity function on the cluster performance of SC-ICNE is discussed,and compare the cluster performance of SC-ICNE with the k-means algorithm in non-spherical convex data set and the UCI data set. Simulation results show that the SC-ICNE algorithm achieves high performance in terms of clustering accuracy and identifying the cluster number.

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更新日期/Last Update: 2015-10-16