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

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