Smoothed self-organizing map for robust clustering
Smoothed self-organizing map for robust clustering
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DOI：
10.1016/j.ins.2019.06.038
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发表时间：
2020-02-01
影响因子：
8.1
通讯作者：
Massari, Riccardo
中科院分区：
文献类型：
--
作者：
D'Urso, Pierpaolo;De Giovanni, Livia;Massari, Riccardo
本文提出了一种对离群点具有鲁棒性的自组织映射（SOM），即平滑SOM（S-SOM）。S-SOM通过升级学习规则来改进标准SOM的输入密度映射、矢量量化和聚类特性，以便将离群输入矢量平滑地表示到地图上。学习规则的升级是基于输入向量与其最近码本之间的互补指数距离。证明了S-SOM收敛到稳定状态。三个比较模拟研究和数字创新数据的建议性应用程序表明所提出的S-SOM的鲁棒性和有效性。本文的补充材料可用。(C)2019由Elsevier Inc.出版
In this paper a Self-Organizing Map (SOM) robust to the presence of outliers, the Smoothed SOM (S-SOM), is proposed. S-SOM improves the properties of input density mapping, vector quantization, and clustering of the standard SOM in the presence of outliers by upgrading the learning rule in order to smooth the representation of outlying input vectors onto the map. The upgrade of the learning rule is based on the complementary exponential distance between the input vector and its closest codebook. The convergence of the S-SOM to a stable state is proved. Three comparative simulation studies and a suggestive application to digital innovation data show the robustness and effectiveness of the proposed S-SOM. Supplementary materials for this article are available. (C) 2019 Published by Elsevier Inc.