Abstract
The high efficiency and quality of clustering for dealing with high-dimensional data are strongly needed with the leap of data scale. Density-based clustering is an effective clustering approach, and its representative algorithm DBSCAN has advantages as clustering with arbitrary shapes and handling noise. However, it also has disadvantages in its high time expense, parameter tuning and inability to varying densities. In this paper, a new clustering algorithm called VDSCHT (Varying Density Spatial Clustering Based on a Hierarchical Tree) is presented that constructs a hierarchical tree to describe subcluster and tune local parameter dynamically. Density-based clustering is adopted to cluster by detecting adjacent spaces of the tree. Both theoretical analysis and experimental results indicate that VDSCHT not only has the advantages of density-based clustering, but can also tune the local parameter dynamically to deal with varying densities. In addition, only one scan of database makes it suitable for mining large-scaled ones.
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Hu, X., Wang, D., Wu, X. (2007). Varying Density Spatial Clustering Based on a Hierarchical Tree. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_15
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DOI: https://doi.org/10.1007/978-3-540-73499-4_15
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