Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10362237 | Pattern Recognition Letters | 2005 | 12 Pages |
Abstract
This new clustering method GCHL (a Grid-Clustering algorithm for High-dimensional very Large spatial databases) combines a novel density-grid based clustering with axis-parallel partitioning strategy to identify areas of high density in the input data space. The algorithm work as well in the feature space of any data set. The method operates on a limited memory buffer and requires at most a single scan through the data. We demonstrate the high quality of the obtained clustering solutions, capability of discovering concave/deeper and convex/higher regions, their robustness to outlier and noise, and GCHL excellent scalability.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
A.H. Pilevar, M. Sukumar,