Abstract
This paper proposes a special adaptive mean shift clustering algorithm, especially for the case of highly overlapping clusters. Its application is demonstrated for simulated data, aiming at finding the ‘old clusters’. The obtained clustering result is actually close to an estimated upper bound, derived for those simulated data elsewhere.
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Li, F., Klette, R. (2009). A Variant of Adaptive Mean Shift-Based Clustering. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_122
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DOI: https://doi.org/10.1007/978-3-642-02490-0_122
Publisher Name: Springer, Berlin, Heidelberg
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