| 摘要 |
A grid-based data clustering method comprises: a parameter setting step, a partition step, a searching step, a seed-classifying step, an extension step, and a termination step. Through the above-mentioned steps, data in a data set are disposed in a plurality of grids, and the grids are classified into dense grids and uncrowded grids for a cluster to extend from one of the dense grid to gradually combine data in other dense grids nearby. Consequently, convenience in parameter setting, efficiency and accuracy in data clustering, and performance in noise filtering are achieved.
|
