Self-organizing map for clustering in the graph domain

被引：61

作者：

Günter, S ^{[1
]}

Bunke, H ^{[1
]}

机构：

[1] Univ Bern, Dept Comp Sci, CH-3012 Bern, Switzerland

来源：

PATTERN RECOGNITION LETTERS | 2002年 / 23卷 / 04期

关键词：

self-organizing map; structural pattern recognition; graph matching; graph edit distance; graph clustering; neuron utility;

D O I：

10.1016/S0167-8655(01)00173-8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Self-organizing map (som) is a flexible method that can be applied to various tasks in pattern recognition. However it is limited in the sense that it uses only pattern representations in terms of feature vectors. It was only recently that an extension to strings was proposed. In the present paper we go a step further and present a version of som that works in the domain of graphs. Graphs are a powerful data structure that include pattern representations based on strings and feature vectors as special cases. After introducing the new method a number of experiments will be described demonstrating its feasibility in the context of a graph clustering task. (C) 2002 Elsevier Science B.V. All rights reserved.

引用

页码：405 / 417

页数：13

共 50 条

[31] An extended self-organizing map (ESOM) for hierarchical clustering
Hashemi, R
Bahar, M
De Agostino, S
[J]. INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOL 1-4, PROCEEDINGS, 2005, : 2856 - 2860
[32] Visual clustering of trademarks using the self-organizing map
Hussain, M
Eakins, J
Sexton, G
[J]. IMAGE AND VIDEO RETRIEVAL, 2002, 2383 : 147 - 156
[33] The self-organizing map
Kohonen, T
[J]. NEUROCOMPUTING, 1998, 21 (1-3) : 1 - 6
[34] The self-organizing map
Helsinki University of Technology, Neural Networks Res. Ctr., P.O. B., FIN-02015 HUT, Finland
[J]. Neurocomputing, 1-3 (1-6):
[35] Inspection planning in the polygonal domain by Self-Organizing Map
Faigl, Jan
Preucil, Libor
[J]. APPLIED SOFT COMPUTING, 2011, 11 (08) : 5028 - 5041
[36] THE SELF-ORGANIZING MAP
KOHONEN, T
[J]. PROCEEDINGS OF THE IEEE, 1990, 78 (09) : 1464 - 1480
[37] Comparison of visualization of optimal clustering using self-organizing map and growing hierarchical self-organizing map in cellular manufacturing system
Chattopadhyay, Manojit
Dan, Pranab K.
Mazumdar, Sitanath
[J]. APPLIED SOFT COMPUTING, 2014, 22 : 528 - 543
[38] Fusion of self-organizing map and granular self-organizing map for microblog summarization
Naveen Saini
Sriparna Saha
Sahil Mansoori
Pushpak Bhattacharyya
[J]. Soft Computing, 2020, 24 : 18699 - 18711
[39] Fusion of self-organizing map and granular self-organizing map for microblog summarization
Saini, Naveen
Saha, Sriparna
Mansoori, Sahil
Bhattacharyya, Pushpak
[J]. SOFT COMPUTING, 2020, 24 (24) : 18699 - 18711
[40] A Study on Clustering Method by Self-Organizing Map and Information Criteria
Kato, Satoru
Horiuchi, Tadashi
Itoh, Yoshio
[J]. NEURAL INFORMATION PROCESSING, PT 2, PROCEEDINGS, 2009, 5864 : 874 - +

← 1 2 3 4 5 →