Varying Density Spatial Clustering Based on a Hierarchical Tree
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Density-based clustering of data streams at multiple resolutions
ACM Transactions on Knowledge Discovery from Data (TKDD)
Improving DBSCAN's execution time by using a pruning technique on bit vectors
Pattern Recognition Letters
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part I
DGCL: an efficient density and grid based clustering algorithm for large spatial database
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
Spatial Data Mining for Highlighting Hotspots in Personal Navigation Routes
International Journal of Data Warehousing and Mining
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Clustering in data mining is used for identifying useful patterns and interesting distributions in the underlying data. Several algorithms for clustering large data sets have been proposed in the literature using different techniques. Density-based method is one of these methodologies which can detect arbitrary shaped clusters where clusters are defined as dense regions separated by low density regions. In this paper, we present a new clustering algorithm to enhance the density-based algorithm DBSCAN. Synthetic datasets are used for experimental evaluation which shows that the new clustering algorithm is faster and more scalable than the original DBSCAN.