… improvement in query speed, it is still insufficient to handle high-dimensional data efficiently
… gap, a Grid-Clustering algorithm for High-dimensional very Large spatial databases (GCHL) …

… the sparsity of a high-dimensional data stream. When a data stream enters the algorithm,
the irregular grid … Many current “density-grid clustering” algorithms are unable to control …

… It can quickly find clusters of any shape in a noisy spatial database, without … high-dimensional
data more efficient. This article focuses more on the applicability of the DBSCAN algorithm …

… OptiGrid is a typical dynamic grid clustering algorithm, which is built on the spatial data … of
most of algorithms in high-dimensional data. Meanwhile, the speed of grid clustering will not be …

… CLIQUE, grid-clustering technique for high-dimensional very large spatial databases (GCHL), …
In Proceedings of the 23rd International Conference on Very Large Data Bases, Athens, …

… Optimal grid-clustering: Towards breaking the curse of dimensionality in high-dimensional
clustering… STING: A statistical information grid approach to spatial data mining. In Twenty-third …

… Next, we consider the HK-means algorithm for clustering high-dimensional data. We
validate the performance of the proposed new algorithm with three high-dimensional datasets …

… One aims to improve the execution of algorithm and optimize parameter setting, such as GCHL
[… , “GCHL: A grid-clustering algorithm for high-dimensional very large spatial data bases,” …

… • There exist problems when dealing with high dimensional data since it became more difficult
to … , “Gchl: A grid-clustering algorithm for high-dimensional very large spatial data bases,” …

… It is expected for new clustering algorithms to find the appropriate number of clusters when
dealing with complex data, meaning various shapes and densities. They also have to be self-…