Generalized Self-Organizing Maps for Automatic Determination of the Number of Clusters and Their Multiprototypes in Cluster Analysis
Generalized Self-Organizing Maps for Automatic Determination of the Number of Clusters and Their Multiprototypes in Cluster Analysis
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DOI：
10.1109/tnnls.2017.2704779
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发表时间：
2018-07-01
影响因子：
10.4
通讯作者：
Rudzinski, Filip
中科院分区：
文献类型：
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作者：
Gorzalczany, Marian B.;Rudzinski, Filip
本文提出了一维邻域(神经元链)自组织映射的推广，可有效地应用于复杂的聚类分析问题。这种泛化的本质在于引入了一种机制，允许神经元链在学习期间断开为子链，重新连接一些子链，并动态调节系统中神经元的总数。这些功能使网络能够以完全无监督的方式工作(即，使用没有预定义数量的集群的未标记数据)，以自动生成能够代表数据集中的广泛集群的多原型集合。首先，在一些合成数据集上说明了所提出的方法的操作。然后，使用从加州大学欧文分校(UCI)机器学习储存库获得的几个真实的、复杂的和多维的基准数据集和基于进化学习数据集储库的知识提取来测试该技术。文中还分析了该方法对控制参数变化的敏感性，并与另一种方法进行了比较分析。
This paper presents a generalization of selforganizing maps with 1-D neighborhoods (neuron chains) that can be effectively applied to complex cluster analysis problems. The essence of the generalization consists in introducing mechanisms that allow the neuron chain-during learning-to disconnect into subchains, to reconnect some of the subchains again, and to dynamically regulate the overall number of neurons in the system. These features enable the network-working in a fully unsupervised way (i.e., using unlabeled data without a predefined number of clusters)-to automatically generate collections of multiprototypes that are able to represent a broad range of clusters in data sets. First, the operation of the proposed approach is illustrated on some synthetic data sets. Then, this technique is tested using several real-life, complex, and multidimensional benchmark data sets available from the University of California at Irvine (UCI) Machine Learning repository and the Knowledge Extraction based on Evolutionary Learning data set repository. A sensitivity analysis of our approach to changes in control parameters and a comparative analysis with an alternative approach are also performed.