Summary
Clustering is one of the data mining techniques that extracts knowledge from spatial datasets. DBSCAN algorithm was considered as well-founded algorithm as it discovers clusters in different shapes and handles noise effectively. There are several algorithms that improve DBSCAN as fast hybrid density algorithm (L-DBSCAN) and fast density-based clustering algorithm. In this paper, an enhanced algorithm is proposed that improves fast density-based clustering algorithm in the ability to discover clusters with different densities and clustering large datasets.
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El-Sonbaty, Y., Said, H. (2009). Enhanced Density Based Algorithm for Clustering Large Datasets. In: Kurzynski, M., Wozniak, M. (eds) Computer Recognition Systems 3. Advances in Intelligent and Soft Computing, vol 57. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93905-4_24
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DOI: https://doi.org/10.1007/978-3-540-93905-4_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-93904-7
Online ISBN: 978-3-540-93905-4
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