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
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
ACODF: a novel data clustering approach for data mining in large databases
Journal of Systems and Software - Special issue: Performance modeling and analysis of computer systems and networks
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Density-based clustering for real-time stream data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
ANGEL: a new effective and efficient hybrid clustering technique for large databases
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
G-TREACLE: a new grid-based and tree-alike pattern clustering technique for large databases
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
KIDBSCAN: a new efficient data clustering algorithm
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Improving DBSCAN's execution time by using a pruning technique on bit vectors
Pattern Recognition Letters
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Cluster analysis in data mining and knowledge discovery is an essential business application. This investigation describes a new clustering approach named EIDBSCAN that extends expansion seed selection into a sampling-based DBSCAN clustering algorithm. Additionally, the proposed algorithm may reduce eight Marked Boundary Objects to add expansion seeds according to far centrifugal force, which increases coverage. Our experimental results reveal that the proposed EIDBSCAN yields more accurate clustering results. In addition, in all the cases we studied, the proposed approach has a lower execution time cost than several existing well-known approaches, such as DBSCAN, IDBSCAN and KIDBSCAN clustering algorithms.