AGRID: An efficient algorithm for clustering large high-dimensional datasets. The clustering algorithm GDILC relies on density-based clustering with grid and is designed to discover clusters of arbitrary shapes and eliminate noises. However, it is not scalable to large high-dimensional datasets. In this paper, we improved this algorithm in five important directions. Through these improvements, AGRID is of high scalability and can process large high-dimensional datasets. It can discover clusters of various shapes and eliminate noises effectively. Besides, it is insensitive to the order of input and is a non-parametric algorithm. The high speed and accuracy of the AGRID clustering algorithm was shown in our experiments.
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References in zbMATH (referenced in 2 articles , 1 standard article )
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- Tran Van Long; Linsen, Lars: Visualizing high density clusters in multidimensional data using optimized star coordinates (2011)
- Zhao, Yanchang; Song, Junde: AGRID: An efficient algorithm for clustering large high-dimensional datasets (2003)