Abstract
In this work, we present a clustering algorithm to find clusters of different sizes, shapes and densities, to deal with overlapping cluster distributions and background noise. The algorithm is divided in two stages. In a first step, local density is estimated at each data point. In a second stage, a hierarchical approach is used by merging clusters according to the introduced cluster distance, based on heuristic measures about how modes overlap in a distribution. Experimental results on synthetic and real databases show the validity of the method.
This work has been partially supported by projects ESP2005-07724-C05-05 and TIC2003-08496 from the Spanish CICYT.
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© 2006 Springer-Verlag Berlin Heidelberg
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Pascual, D., Pla, F., Sánchez, J.S. (2006). Non Parametric Local Density-Based Clustering for Multimodal Overlapping Distributions. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_81
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DOI: https://doi.org/10.1007/11875581_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
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