CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
| Hi-index | 0.00 |
In this paper, we propose an efficient and effective clustering method that requires to scan a dataset only once. The original dataset is transformed first and merged into a hyper image of controllable size. Unlike traditional methods, the dissimilarity measurement between objects is calculated once for all objects by using various image processing methodologies, such as morphological operations. Image connect component extraction is thereby used to extract clusters from the hyper image. The proposed method is easy to use for clustering data in way of fuzzy and hierarchical fashion readily under a single dataset scan. It is also efficient for incremental and dynamic clustering without additional scan of the original dataset. Experimental results show that the proposed method is robust and stable under various parameter settings such that it is more effective and useful than traditional clustering methods, especially for very large datasets.