Multidimensional binary search trees used for associative searching
Communications of the ACM
Approaches for scaling DBSCAN algorithm to large spatial databases
Journal of Computer Science and Technology
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Experiments in Parallel Clustering with DBSCAN
Euro-Par '01 Proceedings of the 7th International Euro-Par Conference Manchester on Parallel Processing
A novel genetic algorithm for automatic clustering
Pattern Recognition Letters
A hamming distance based VLIW/EPIC code compression technique
Proceedings of the 2004 international conference on Compilers, architecture, and synthesis for embedded systems
An Efficient Density Based Clustering Algorithm for Large Databases
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Optimizing bitmap indices with efficient compression
ACM Transactions on Database Systems (TODS)
Effective clustering and boundary detection algorithm based on Delaunay triangulation
Pattern Recognition Letters
DIVFRP: An automatic divisive hierarchical clustering method based on the furthest reference points
Pattern Recognition Letters
An Improved Clustering Algorithm
ISCID '08 Proceedings of the 2008 International Symposium on Computational Intelligence and Design - Volume 01
Robot Brains: Circuits and Systems for Conscious Machines
Robot Brains: Circuits and Systems for Conscious Machines
An adaptive flocking algorithm for performing approximate clustering
Information Sciences: an International Journal
Rough-DBSCAN: A fast hybrid density based clustering method for large data sets
Pattern Recognition Letters
Sorting improves word-aligned bitmap indexes
Data & Knowledge Engineering
EIDBSCAN: An Extended Improving DBSCAN algorithm with sampling techniques
International Journal of Business Intelligence and Data Mining
Continuous K-Means Monitoring with Low Reporting Cost in Sensor Networks
IEEE Transactions on Knowledge and Data Engineering
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
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Clustering is the process of assigning a set of physical or abstract objects into previously unknown groups. The goal of clustering is to group similar objects into the same clusters and dissimilar objects into different clusters. Similarities between objects are evaluated by using the attribute values of objects. There are many clustering algorithms in the literature; among them, DBSCAN is a well known density-based clustering algorithm. We improve DBSCAN's execution time performance for binary data sets and Hamming distances. We achieve considerable speed gains by using a novel pruning technique, as well as bit vectors, and binary operations. Our novel method effectively discards distant neighbors of an object and computes only the distances between an object and its possible neighbors. By discarding distant neighbors, we avoid unnecessary distance computations and use less CPU time when compared with the conventional DBSCAN algorithm. However, the accuracy of our method is identical to that of the original DBSCAN. Experimental test results on real and synthetic data sets demonstrate that, by using our pruning technique, we obtain considerably faster execution time results compared to DBSCAN.