The neuron closest to the input data point, determined by a metric distance such as Euclidean distance.
Published in Chapter:
Exploratory Cluster Analysis Using Self-Organizing Maps: Algorithms, Methodologies, and Framework
Nuno C. Marques (NOVA-LINCS, SST, Universidade NOVA de Lisboa, Portugal) and Bruno Silva (EST, Polytechnic Institute of Setúbal, Portugal)
Copyright: © 2023
|Pages: 27
DOI: 10.4018/978-1-6684-9591-9.ch010
Abstract
As the volume and complexity of data streams continue to increase, exploratory cluster analysis is becoming increasingly important. In this chapter, the authors explore the use of artificial neural networks (ANNs), particularly self-organizing maps (SOMs), for this purpose. They propose additional methodologies, including concept drift detection, as well as distributed and collaborative learning strategies and introduce a new open-source Java ANN library, designed to support practical applications of SOMs across various domains. By following our tutorial, users will gain practical insights into visualizing and analyzing these challenging datasets, enabling them to harness the full potential of our approach in their own projects. Overall, this chapter aims to provide readers with a comprehensive understanding of SOMs and their place within the broader context of artificial neural networks. Furthermore, we offer practical guidance on the effective development and utilization of these models in real-world applications.