Self-organizing Maps as substitutes for K-means clustering

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126 Citations (Scopus)


One of the most widely used clustering techniques used in GISc problems is the k-means algorithm. One of the most important issues in the correct use of k-means is the initialization procedure that ultimately determines which part of the solution space will be searched. In this paper we briefly review different initialization procedures, and propose Kohonen's Self-Organizing Maps as the most convenient method, given the proper training parameters. Furthermore, we show that in the final stages of its training procedure the Self-Organizing Map algorithms is rigorously the same as the k-means algorithm. Thus we propose the use of Self-Organizing Maps as possible substitutes for the more classical k-means clustering algorithms.

Original languageEnglish
Title of host publicationComputational Science – ICCS 2005
Subtitle of host publication5th International Conference, Atlanta, GA, USA
Number of pages8
ISBN (Print)978-3-540-26044-8
Publication statusPublished - 2005
Event5th International Conference on Computational Science, 2005 - Atlanta, Georgia
Duration: 22 May 200525 May 2005


Conference5th International Conference on Computational Science, 2005


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