AGRID: An efficient algorithm for clustering large high-dimensional datasets

Abstract

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.