Abstract
A good approach in data mining is density based clustering in which the clusters are constructed based on the density of shape regions. The prominent algorithm proposed in density based clustering family is DBSCAN [1] that uses two global density parameters, namely minimum number of points for a dense region and epsilon indicating the neighborhood distance. Among others, one of the weaknesses of this algorithm is its un-suitability for multi-density data sets where different regions have various densities so the same epsilon does not work. In this paper, a new density based clustering algorithm, MSDBSCAN, is proposed. MSDBSCAN uses a new definition for core point and dense region. The MSDBSCAN can find clusters in multi-variant density data sets. Also this algorithm benefits scale independency. The results obtained on data sets show that the MSDBSCAN is very effective in multi-variant environment.
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Esfandani, G., Abolhassani, H. (2010). MSDBSCAN: Multi-density Scale-Independent Clustering Algorithm Based on DBSCAN. In: Cao, L., Feng, Y., Zhong, J. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6440. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17316-5_19
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DOI: https://doi.org/10.1007/978-3-642-17316-5_19
Publisher Name: Springer, Berlin, Heidelberg
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