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Titlebook: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization; Proceedings of the 1 Alfredo Vellido,Kar

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期刊全称Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization
期刊简称Proceedings of the 1
影响因子2023Alfredo Vellido,Karina Gibert,José David Martín Gu
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发行地址Covers the latest theoretical developments in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization.Presents computational aspects and applications for data mining and
学科分类Advances in Intelligent Systems and Computing
图书封面Titlebook: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization; Proceedings of the 1 Alfredo Vellido,Kar
影响因子.This book gathers papers presented at the 13th International Workshop on Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization (WSOM+), which was held in Barcelona, Spain, from the 26th to the 28th of June 2019. Since being founded in 1997, the conference has showcased the state of the art in unsupervised machine learning methods related to the successful and widely used self-organizing map (SOM) method, and extending its scope to clustering and data visualization. In this installment of the AISC series, the reader will find theoretical research on SOM, LVQ and related methods, as well as numerous applications to problems in fields ranging from business and engineering to the life sciences. Given the scope of its coverage, the book will be of interest to machine learning researchers and practitioners in general and, more specifically, to those looking for the latest developments in unsupervised learning and data visualization..
Pindex Conference proceedings 2020
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When Clustering the Multiscalar Fingerprint of the City Reveals Its Segregation Patterns specific measures for assessing features contributions to clusters, to explore this complex object and to single out . of segregation. We illustrate how clustering allows to see where, how and to which extent segregation occurs.
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2194-5357 computational aspects and applications for data mining and .This book gathers papers presented at the 13th International Workshop on Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization (WSOM+), which was held in Barcelona, Spain, from the 26th to the 28th of June 2
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Yi Xiong,Xiaolei Zhu,Hao Dai,Dong-Qing Weie thus local and distributed. In this paper we present performance results showing than CSOM can obtain faster and better quantisation than classical SOM when used on high-dimensional vectors. We also present an application on video compression based on vector quantisation, in which CSOM outperforms SOM.
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