Abstract
In the paper, we propose a novel parameter free approach for clustering analysis. The approach needs not to make assumptions or define parameters on the cluster number or the results, while the clustered results are visually verified and approved by experimental work. For simplicity, this paper demonstrates the idea using Fuzzy C-Means (FCMs) clustering method, but the proposed open framework allows easy integration with other clustering methods. The method-independent framework generates optimal clustering results and avoids intrinsic biases from individual clustering methods.
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Huang, H., Mok, Py., Kwok, Yl., Au, SC. (2009). A Parameter Free Approach for Clustering Analysis. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_99
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DOI: https://doi.org/10.1007/978-3-642-03767-2_99
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03766-5
Online ISBN: 978-3-642-03767-2
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