BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 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
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd 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
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
| Hi-index | 0.00 |
In this paper, we introduce SFCLUS (signal filter based clustering algorithm) an effective and efficient approach to the clustering problem. Using the concept of signal filter to reduce the noise level and find the approximate locations of clusters. A new mathematics morphological clustering operator is designed to discover clusters around those locations. The combination of signal filtering and mathematics morphology can achieve high accurate clustering result and insensitive to the grid size. In contrast to existing approaches, SFCLUS is able to detect arbitrarily shaped clusters; it is very efficient with a complexity of O(N); it can distinguish clusters of different density; it is insensitive to large amounts of noise; it has a determinate result, insensitive with respect to input data; it is not sensitive to the grid size.