Abstract
Clustering in large database is an important and useful data mining activity. It expects to find out meaningful patterns among the data set. Some new requirements of clustering have been raised : good efficiency for large database; easy to determine the input parameters; separate noise from the clusters [1]. However, conventional clustering algorithms seldom can fulfill all these requirements. The notion of density-based clustering has been proposed which satisfies all these requirements
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© 1999 Springer-Verlag Berlin Heidelberg
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Zhou, B., Cheung, D.W., Kao, B. (1999). A Fast Algorithm for Density-Based Clustering in Large Database. In: Zhong, N., Zhou, L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science(), vol 1574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48912-6_45
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DOI: https://doi.org/10.1007/3-540-48912-6_45
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