Abstract
Most clustering algorithms need to preset the initial parameters which affect the performance of clustering very much. To solve this problem, a new method is proposed, which determine the center points of clustering by density-searching according to the universality of the Gaussian distribution. After the center was obtained, the cluster expands based on the correlation coefficient between clusters and the membership of the samples until the terminating condition is met. The experimental results show that this method could classify the samples of Gaussian distribution with different degree of overlap accurately. Compared with the fuzzy c-means algorithm, the proposed method is more accurate and timesaving when applied to the Iris data and Fossil data.
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Tan, L., Liu, Y., Chen, X. (2011). An Expanding Clustering Algorithm Based on Density Searching. In: Zhu, M. (eds) Information and Management Engineering. ICCIC 2011. Communications in Computer and Information Science, vol 236. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24097-3_19
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DOI: https://doi.org/10.1007/978-3-642-24097-3_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24096-6
Online ISBN: 978-3-642-24097-3
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