Topology-Based Clustering Using Polar Self-Organizing Map

Xu Lu; Chow Tommy W. S.; Ma Eden W. M.

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Topology-Based Clustering Using Polar Self-Organizing Map

机译：使用极性自组织图的基于拓扑的聚类

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Cluster analysis of unlabeled data sets has been recognized as a key research topic in varieties of fields. In many practical cases, no knowledge is specified, for example, the number of clusters is unknown. In this paper, grid clustering based on the polar self-organizing map (PolSOM) is developed to automatically identify the optimal number of partitions. The data topology consisting of both the distance and density is exploited in the grid clustering. The proposed clustering method also provides a visual representation as PolSOM allows the characteristics of clusters to be presented as a 2-D polar map in terms of the data feature and value. Experimental studies on synthetic and real data sets demonstrate that the proposed algorithm provides higher clustering accuracy and lower computational cost compared with six conventional methods.

机译：未标记数据集的聚类分析已被认为是各个领域中的关键研究主题。在许多实际情况下，没有指定知识，例如，簇数未知。在本文中，基于极地自组织图（PolSOM）的网格聚类被开发来自动识别最佳分区数。在网格聚类中利用了既包含距离又包含密度的数据拓扑。提议的聚类方法还提供了可视化表示，因为PolSOM允许将聚类的特征显示为数据特征和值的二维极坐标图。对合成和真实数据集的实验研究表明，与六种常规方法相比，该算法提供了更高的聚类精度和更低的计算成本。

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来源
《Neural Networks and Learning Systems, IEEE Transactions on》 |2015年第4期|798-808|共11页
作者
Xu Lu; Chow Tommy W. S.; Ma Eden W. M.;
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作者单位

Department of Electronic Engineering, City University of Hong Kong, Hong Kong;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Clustering algorithms; Couplings; Data visualization; Indexes; Merging; Neurons; Topology; Clustering; polar self-organizing map (PolSOM); unsupervised learning; visualization;

机译：聚类算法;耦合;数据可视化;索引;合并;Neurons;拓扑;聚类;极性自组织图（PolSOM）;无监督学习;可视化;

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1. Topology-Based Hierarchical Clustering of Self-Organizing Maps [J] . Neural Networks, IEEE Transactions on . 2011,第3期

机译：自组织图的基于拓扑的层次聚类
2. Comparison of visualization of optimal clustering using self-organizing map and growing hierarchical self-organizing map in cellular manufacturing system [J] . Chattopadhyay Manojit, Dan Pranab K., Mazumdar Sitanath Applied Soft Computing . 2014,第Null期

机译：自组织图和增长层次自组织图在蜂窝制造系统中最佳聚类可视化的比较
3. Two-Tier Mapper, an unbiased topology-based clustering method for enhanced global gene expression analysis [J] . Jeitziner Rachel, Carriere Mathieu, Rougemont Jacques, Bioinformatics . 2019,第18期

机译：两层映射器，一种基于无偏的基于拓扑的聚类方法，用于增强全局基因表达分析
4. Using Andrews Curves for Clustering and Sub-clustering Self-Organizing Maps [C] . Cesar Garcia-Osorio, Jesus Maudes, Colin Fyfe European Symposium on Artificial Neural Networks . 2004

机译：使用Andrews曲线进行群集和分类自组织地图
5. The use of data topology in unsupervised clustering of high-dimensional data with self-organizing maps. [D] . Tasdemir, Kadim. 2008

机译：在具有自组织映射的高维数据的无监督聚类中使用数据拓扑。
6. JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters [O] . Hong Seo Lim, Peng Qiu 2021

机译：JSOM：共同不断发展的自组织地图用于对准生物数据集和相关集群的识别
7. U*F clustering: a new performant "cluster-mining" method based on segmentation of Self-Organizing Maps [O] . Moutarde Fabien, Ultsch Alfred 2005

机译：U * F聚类：一种基于自组织图分割的新型性能“聚类挖掘”方法

Topology-Based Clustering Using Polar Self-Organizing Map

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