Article ID Journal Published Year Pages File Type
10362237 Pattern Recognition Letters 2005 12 Pages PDF
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
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