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
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
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Unsupervised Anomaly Detection Using HDG-Clustering Algorithm
Neural Information Processing
Discovering potential musical instruments teachers using data clustering approach
NN'09 Proceedings of the 10th WSEAS international conference on Neural networks
NPUST: An Efficient Clustering Algorithm Using Partition Space Technique for Large Databases
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
WSEAS Transactions on Computers
EIDBSCAN: An Extended Improving DBSCAN algorithm with sampling techniques
International Journal of Business Intelligence 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
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Spatial data clustering plays an important role in numerous fields. Data clustering algorithms have been developed in recent years. K-means is fast, easily implemented and finds most local optima. IDBSCAN is more efficient than DBSCAN. IDBSCAN can also find arbitrary shapes and detect noisy points for data clustering. This investigation presents a new technique based on the concept of IDBSCAN, in which K-means is used to find the high-density center points and then IDBSCAN is used to expand clusters from these high-density center points. IDBSCAN has a lower execution time because it reduces the execution time by selecting representative points in seeds. The simulation indicates that the proposed KIDBSCAN yields more accurate clustering results. Additionally, this new approach reduces the I/O cost. KIDBSCAN outperforms DBSCAN and IDBSCAN.