Algorithm5: A Technique for Fuzzy Similarity Clustering of Chemical Inventories
Autor: | John M. Cibulskis, and Patrick Dale McCray, Michael Cibulskis, Thompson N. Doman, Dale P. Spangler |
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Rok vydání: | 1996 |
Předmět: |
Clustering high-dimensional data
Fuzzy clustering Correlation clustering Constrained clustering General Chemistry computer.software_genre Computer Science Applications Data stream clustering Computational Theory and Mathematics CURE data clustering algorithm Canopy clustering algorithm Data mining Cluster analysis computer Information Systems Mathematics |
Zdroj: | Journal of Chemical Information and Computer Sciences. 36:1195-1204 |
ISSN: | 1520-5142 0095-2338 |
Popis: | Clustering of chemical inventories on the basis of structural similarity has been shown to be useful in a number of applications related to the utilization and enhancement of those inventories. However, the widely-used Jarvis−Patrick clustering algorithm displays a number of weaknesses which make it difficult to cluster large databases in a consistent, satisfactory, and timely manner. Jarvis−Patrick clusters tend to be either too large and heterogeneous (i.e., “chained”) or too small and exclusive (i.e., under-clustered), and the algorithm requires time-consuming manual tuning. This paper describes a computer algorithm which is nondirective, in that it performs the clustering without manual tuning yet generates useful clustering results. |
Databáze: | OpenAIRE |
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