Distance-based methods such as UPGMA (Unweighted Pair Group Method with Arithmetic Mean) continue to play a significant role in phylogenetic research. We use polyhedral combinatorics to analyze the natural subdivision of the positive orthant induced by classifying the input vectors according to tree topologies returned by the algorithm. The partition lattice informs the study of UPGMA trees. We...

Department of Computer Science, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu City 30013, Taiwan Department of Computer Science and Information Engineering, Chang Gung University, No. 259, Sanmin Rd., Guishan Township, Taoyuan City 33302, Taiwan Department of Computer Science and Communication Engineering, Providence University, No. 200, Sec. 7, Taiwan Boulevard, Sha...

The genetic diversity study has paramount importance in breeding programs; hence, it allows selection and choice of the parental genetic divergence, which have the agronomic traits desired by the breeder. This study aimed to characterize the genetic divergence between 24 soybean genotypes through their agronomic traits, using multivariate clustering methods to select the potential genitors for ...

Eucommia ulmoides Oliver, the only extant species of Eucommiaceae, is a second-category state-protected endangered plant in China. Evaluation of genetic diversity among some intraspecific hybrid populations of E. ulmoides Oliver is vital for breeding programs and further conservation of this rare species. We studied the genetic diversity of 130 accessions from 13 E. ulmoides intraspecific hybri...

A formula is given for a flexible combinatorial clustering strategy, having UPGMA and ISS as special cases. When numerical clustering methods are used to investigate the structure of taxonomic groups, it is common practice to try several methods, having different clustering intensities. For example, one might use single linkage (weak), UPGMA (medium), and complete linkage (strong) (Sneath and S...

MOTIVATION UPGMA (average linking) is probably the most popular algorithm for hierarchical data clustering, especially in computational biology. However, UPGMA requires the entire dissimilarity matrix in memory. Due to this prohibitive requirement, UPGMA is not scalable to very large datasets. APPLICATION We present a novel class of memory-constrained UPGMA (MC-UPGMA) algorithms. Given any pr...