Algorithm5:  A Technique for Fuzzy Similarity Clustering of Chemical Inventories

This is the authors' abstract. We don't add key points for this paper.

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.