Clustering with artificial neural networks and traditional techniques
Clustering with artificial neural networks and traditional techniques
复制标题
使用人工神经网络和传统技术进行聚类
DOI：
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发表时间：
2003
影响因子：
7
通讯作者：
D. Tambouratzis
中科院分区：
文献类型：
--
作者：
G. Tambouratzis;Tatiana Tambouratzis;D. Tambouratzis
在本文中，引入了两种基于神经网络的聚类技术。架构和大量节点在描述其聚类特征和潜力后，与经典的统计技术进行了比较。相应的统计方法使用的每个神经元网络聚类技术与特定指标之间的对应关系，这些统计方法尤其反映了聚类模式的亲和力，发现HTN可以执行类似于最佳统计方法的聚类任务另一方面，它进一步提出了最佳数量的组。在不考虑处理时间的情况下，要增加较高的模式。
In this article, two clustering techniques based on neural networks are introduced. The two neural network models are the Harmony theory network (HTN) and the self‐organizing logic neural network (SOLNN), both of which are characterized by parallel processing, a distributed architecture, and a large number of nodes. After describing their clustering characteristics and potential, a comparison to classical statistical techniques is performed. This comparison allows the creation of a correspondence between each neural network clustering technique and particular metrics as used by the corresponding statistical methods, which reflect the affinity of the clustered patterns. In particular, the HTN is found to perform the clustering task with an accuracy similar to the best statistical methods, while it is further capable of proposing an optimal number of groups into which the patterns may be clustered. On the other hand, the SOLNN combines a high clustering accuracy with the ability to cluster higher‐dimensional patterns without a considerable increase in the processing time. © 2003 Wiley Periodicals, Inc.