Abstract
Bayesian Information Criterion (BIC) is a promising method for detecting the number of clusters. It is often used in model-based clustering in which a decisive first local maximum is detected as the number of clusters. In this paper, we re-formulate the BIC in partitioning based clustering algorithm, and propose a new knee point finding method based on it. Experimental results show that the proposed method detects the correct number of clusters more robustly and accurately than the original BIC and performs well in comparison to several other cluster validity indices.
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Zhao, Q., Hautamaki, V., Fränti, P. (2008). Knee Point Detection in BIC for Detecting the Number of Clusters. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_60
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DOI: https://doi.org/10.1007/978-3-540-88458-3_60
Publisher Name: Springer, Berlin, Heidelberg
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