Abstract
When clustering a dataset, the right number k of clusters is not often obvious. And choosing k automatically is a complex problem. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Then, an improved algorithm is presented for learning k while clustering. The algorithm is based on coefficients α, β that affect this selection. Meanwhile, a new measure is suggested to confirm the member of clusters. Finally, we evaluate the computational complexity of the algorithm, apply to real datasets and results show its efficiency.
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Nguyen, D.T., Doan, H. (2012). An Approach to Determine the Number of Clusters for Clustering Algorithms. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34630-9_50
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DOI: https://doi.org/10.1007/978-3-642-34630-9_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34629-3
Online ISBN: 978-3-642-34630-9
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