Grid-clustering: an efficient hierarchical clustering method for very large data sets
- 1 January 1996
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2 (10514651) , 101-105 vol.2
- https://doi.org/10.1109/icpr.1996.546732
Abstract
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.Keywords
This publication has 6 references indexed in Scilit:
- Grid-clustering: an efficient hierarchical clustering method for very large data setsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1996
- Strategies for efficient incremental nearest neighbor searchPattern Recognition, 1990
- Analysis of grid file algorithmsBIT Numerical Mathematics, 1985
- The Grid FileACM Transactions on Database Systems, 1984
- Clustering Methodologies in Exploratory Data AnalysisPublished by Elsevier ,1980
- A heuristic clustering algorithm using union of overlapping pattern-cellsPattern Recognition, 1979