Abstract
In data analysis techniques, the capability of SOM and K-means for clustering large-scale databases has already been confirmed. The most remarkable advantage of SOM-based two-stage methods is in saving time without the considerable computations required by conventional clustering methods for large and complicated data sets. In this research, we propose and evaluate a two-stage clustering method, which combines an ant-based SOM and K-means. The ant-based SOM clustering model, ABSOM, embeds the exploitation and exploration rules of state transition into the conventional SOM algorithm to avoid falling into local minima. After application to four practical data sets, the ABSOM itself not only performs better than Kohonen’s SOM but also it works very well in the two-stage clustering analysis when it is taken as the preprocessing technique for the ABSOM+K-means method.
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Chi, SC., Yang, C.C. (2006). Integration of Ant Colony SOM and K-Means for Clustering Analysis. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_1
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DOI: https://doi.org/10.1007/11892960_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46535-5
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