.png){kind=icon}
返回
Hierarchical growing cell structures: TreeGCS
.png){kind=icon}
OA
AI
DOI：10.1109/69.917561.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We propose a hierarchical clustering algorithm (TreeGCS) based upon the Growing Cell Structure (GCS) neural network of Fritzke. Our algorithm refines and builds upon the GCS base, overcoming an inconsistency in the original GCS algorithm, where the network topology is susceptible to the ordering of the input vectors. Our algorithm is unsupervised, flexible, and dynamic and we have imposed no additional parameters on the underlying GCS algorithm, Our ultimate aim is a hierarchical clustering neural network that is both consistent and stable and identifies the innate hierarchical structure present in vector-based data. We demonstrate improved stability of the GCS foundation and evaluate our algorithm against the hierarchy generated by an ascendant hierarchical clustering dendogram. Our approach emulates the hierarchical clustering of the dendogram. It demonstrates the importance of the parameter settings for GCS and how they affect the stability of the clustering.
Keyword:
unsupervised
growing
neural
network
hierarchical
cluster
topology
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
暂无机构信息
引用论文
Anterior Segment Optical Coherence Tomography Images in Microsporidial Keratoconjunctivitis.png)
Cornea
CorneaIF0
PREAI
分享收藏Avascular Necrosis With Complete Resorption of the Proximal Humerus Following Undisplaced Three-Part FractureOrthopedics
OrthopedicsIF0
PREAI
分享收藏分享收藏SCRIPT-BASED INFERENCE AND MEMORY RETRIEVAL IN SUBSYMBOLIC STORY PROCESSINGAPPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏 没有更多内容
没有更多内容