Grid-clustering: an efficient hierarchical clustering method for very large data sets
E. Schikuta. Pattern Recognition, 1996., Proceedings of the 13th International Conference on(August 1996)
Clustering is a common technique for the analysis of large images.
In this paper a new approach to hierarchical clustering of very large
data sets is presented. The GRIDCLUS algorithm uses a multidimensional
grid data structure to organize the value space surrounding the pattern
values, rather than to organize the patterns themselves. The patterns
are grouped into blocks and clustered with respect to the blocks by a
topological neighbor search algorithm. The runtime behavior of the
algorithm outperforms all conventional hierarchical methods. A
comparison of execution times to those of other commonly used clustering
algorithms, and a heuristic runtime analysis are presented
