[1]钱江波,董逸生.一种基于广度优先搜索邻居的聚类算法[J].东南大学学报(自然科学版),2004,34(1):109-112.[doi:10.3969/j.issn.1001-0505.2004.01.026]
 Qian Jiangbo,Dong Yisheng.A clustering algorithm based on broad first searching neighbors[J].Journal of Southeast University (Natural Science Edition),2004,34(1):109-112.[doi:10.3969/j.issn.1001-0505.2004.01.026]
点击复制

一种基于广度优先搜索邻居的聚类算法()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
34
期数:
2004年第1期
页码:
109-112
栏目:
计算机科学与工程
出版日期:
2004-01-20

文章信息/Info

Title:
A clustering algorithm based on broad first searching neighbors
作者:
钱江波12 董逸生1
1 东南大学计算机科学与工程系, 南京 210096; 2 宁波市公安局, 宁波 315040
Author(s):
Qian Jiangbo12 Dong Yisheng1
1 Department of Computer Science and Engineering, Southeast University, Nanjing 210096, China
2 Ningbo Municipal Public Security Bureau, Ningbo 315040, China
关键词:
聚类分析 广度优先搜索 直接邻居 间接邻居
Keywords:
clustering analysis broad first search direct-neighbor indirect-neighbor
分类号:
TP311.13
DOI:
10.3969/j.issn.1001-0505.2004.01.026
摘要:
聚类算法BFSN广度优先搜索某对象的直接邻居和间接邻居,对符合条件的所有找到的邻居合并,从而完成一类聚类.接着重复该步骤完成所有对象的聚类.与同类算法相比,该算法具有实现简单、复杂度低和容易设定最佳参数等优点.实验证明,在聚类正确率相近的情况下,该算法的效率比较高,而且能揭示同类对象之间的相异程度.
Abstract:
A clustering algorithm named broad first search neighbors(BFSN)searches an objects direct-neighbors and indirect-neighbors based on broad first search. If the found neighbors satisfy the predefined parameters, they can all merge into one cluster. Then the algorithm repeats the procedure to complete clustering. Compared with other algorithms, the BFSN algorithm is easier to realize without complicated calculation. It can define the best parameters without difficulty. The experimental results show that the BFSN algorithm is more effective than some of the others at the same clustering preciseness and it can also reveal dissimilarity degree between the same clusters objects.

参考文献/References:

[1] Huang Zhexue.Extensions to the k-means algorithm for clustering large data sets with categorical values [J].Data Mining and Knowledge Discovery,1998,2(3):283-304.
[2] Karypis G,Han E H,Kumar V.Chameleon:a hierarchical clustering algorithm using dynamic modeling [J].IEEE Computer:Special Issue on Data Analysis and Mining, 1999,32(8):68-75.
[3] Zhang T,Ramakrishnan R,Livny M.Birch:an efficient data clustering method for very large databases[A].In:Proc 1996 ACM-SIGMOD Int Conf on Management of Data[C].Montreal,Canada,1996.103-114.
[4] Ankerst M,Breunig M,Kriegel H P,et al.Optics:ordering points to identify the clustering structure[A].In: Proc 1999 ACM-SIGMOD Int Conf on Management of Data[C].Philadelphia,1999.49-60.
[5] Agrawal R,Gehrke J,Gunopulos D,et al.Automatic subspace clustering of high dimensional data for data mining applications [A].In:Proc 1998 ACM-SIGMOD Int Conf on Management of Data [C].Seattle,1998.94-105.
[6] Jain A K,Murty M N,Flynn P J.Data clustering:a review[J].ACM Computing Surveys, 1999,31(3):264-323.
[7] Han Jiawei,Kamber Micheline.数据挖掘概念与技术[M].范明等译.北京:机械工业出版社,2001.225-230.
[8] Blake C L,Merz C J.Uci repository of machine learning databases [EB/OL].http://www.ics.uci.edu/~mlearn/MLRepository.html.1998/2002-11-15.
[9] MacQueen J.Some methods for classification and analysis of multivariate observations[A].In:Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability[C].Berkeley:University of California Press,1967.281-297.
[10] Cristofor Dana,Simovici Dan A.An information-theoretical approach to clustering categorical databases using genetic algorithms [A].In:Proceedings of the Workshop on Clustering High Dimensional Data and Its Applications [C].Washington,2002.37-46.

备注/Memo

备注/Memo:
作者简介: 钱江波(1974—),男,博士生; 董逸生(联系人),男,教授,博士生导师,ysdong@seu.edu.cn.
更新日期/Last Update: 2004-01-20