Growing self-organizing trees for autonomous hierarchical clustering

被引：5

作者：

Doan, Nhat-Quang ^{[1
]}

Azzag, Hanane ^{[1
]}

Lebbah, Mustapha ^{[1
]}

机构：

[1] Univ Paris 13, CNRS, LIPN, UMR 7030, F-93430 Villetaneuse, France

来源：

NEURAL NETWORKS | 2013年 / 41卷

关键词：

Clustering; Growing network; Data visualization; Self-organizing model; Hierarchical tree;

D O I：

10.1016/j.neunet.2012.08.015

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a new unsupervised learning method based on growing processes and autonomous self-assembly rules. This method, called Growing Self-organizing Trees (GSoT), can grow both network size and tree topology to represent the topological and hierarchical dataset organization, allowing a rapid and interactive visualization. Tree construction rules draw inspiration from elusive properties of biological organization to build hierarchical structures. Experiments conducted on real datasets demonstrate good GSoT performance and provide visual results that are generated during the training process. (c) 2012 Elsevier Ltd. All rights reserved.

引用

页码：85 / 95

页数：11

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