Abstract
This investigation presents a clustering algorithm that incorporates neighbor searching into the density-based IDBSCAN algorithm. The rooted algorithm performs fewer searches than standard IDBSCAN. Experimental results indicate that the proposed MIDBSCAN algorithm has a lower execution time cost than DBSCAN, IDBSCAN or KIDBSCAN. MIDBSCAN has a maximum deviation in clustering correctness rate of 0.1%, and a maximum deviation in noise data filtering rate of 0.3%.
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Tsai, CF., Sung, CY. (2009). MIDBSCAN: An Efficient Density-Based Clustering Algorithm. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_49
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DOI: https://doi.org/10.1007/978-3-642-01216-7_49
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