The SEQUOIA 2000 storage benchmark
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
'1 + 1 2': Merging Distance and Density Based Clustering
DASFAA '01 Proceedings of the 7th International Conference on Database Systems for Advanced Applications
Efficient Density-Based Clustering of Complex Objects
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
An Efficient Density Based Clustering Algorithm for Large Databases
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Automatic Subspace Clustering of High Dimensional Data
Data Mining and Knowledge Discovery
Multi-step density-based clustering
Knowledge and Information Systems
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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.