An Axis-shifted Crossover-Imaged Clustering Algorithm

With low computation time, the grid-based clustering algorithms are efficient clustering algorithms, but the size of the predefined grids and the threshold of the significant cells are seriously influenced their effects. In grid-based clustering system, the data space is partitioned into a finite number of cells to form a grid structure and then performs all clustering operations on this obtained grid structure. The ADCC algorithm is the first one to use axis-shifted strategy to reduce the influences of the size of the cells and inherits the advantage with the low time complexity. But it still uses the cell-clustering twice, the Axis-shifted Crossover-Imaged Clustering Algorithm, called ACICA, is proposed to use cell-clustering only once and still has the same results. The main idea of ACICA algorithm is to shift the original axis in each dimension of the data space after the image of significant cells generated from the original grid structure have been obtained. Because the shifted 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 shifted grid structure will be used to revise and replace the originally obtained significant cells. 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. Key-Words: Data Mining, Grid Structure, Crossover Image, Cell, Shifted Grid, Cater-Corner Significant Cell