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
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 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
WaveCluster: a wavelet-based clustering approach for spatial data in very large databases
The VLDB Journal — The International Journal on Very Large Data Bases
Improving the Orthogonal Range Search k -Windows Algorithm
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
An effective and efficient algorithm for high-dimensional outlier detection
The VLDB Journal — The International Journal on Very Large Data Bases
GCHL: A grid-clustering algorithm for high-dimensional very large spatial data bases
Pattern Recognition Letters
An adaptable deflect and conquer clustering algorithm
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
| Hi-index | 0.00 |
The grid-based clustering algorithm is an efficient clustering algorithm, but its effect is seriously influenced by the size of the predefined grids and the threshold of the significant cells. The data space will be partitioned into a finite number of cells to form a grid structure and then performs all clustering operations on this obtained grid structure. To cluster efficiently and simultaneously, to reduce the influences of the size of the cells and inherits the advantage with the low time complexity, an Adaptive Crossover-Imaged Clustering Algorithm, called ACICA, is proposed in this paper. The main idea of ACICA algorithm is to deflect the original grid structure in each dimension of the data space after the image of significant cells generated from the original grid structure have been obtained. Because the deflected grid structure can be considered a dynamic adjustment of the size of original cells and the threshold of significant cells, the new image generated from this deflected grid structure will be used to revise the originally obtained significant cells. Hence, the new image of significant cells is projected on the original grid structure to be the crossover image. Finally the clusters will be generated from this crossover image. The experimental results verify that, indeed, the effect of ACICA algorithm is less influenced by the size of the cells than other grid-based algorithms. Finally, we will verify by experiment that the results of our proposed ACICA algorithm outperforms than others.