Exploratory cluster analysis using self-organizing maps: Algorithms, methodologies, and framework

Nuno C. Marques, Bruno Silva

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


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.
Original languageEnglish
Title of host publicationPhilosophy of Artificial Intelligence and Its Place in Society
EditorsLuiz Moutinho, Luís Cavique, Enrique Bigné
PublisherIGI Global
Number of pages27
ISBN (Electronic)978-166849593-3
ISBN (Print)978-166849591-9
Publication statusPublished - 16 Oct 2023


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