An algorithm for hierarchical clustering analysis of massive sequence data. To avoid confusion, we note that ESPRIT-Tree is not a program for determining phylogenetic trees, but rather for producing hierarchical clusters of sequences based on sequence similarity, using a tree-like data structure. We extended the concept of space partition used by previous methods for handling sequence data of varying lengths. By assuming that sequence data lives in a pseudometric space, we created a distance-based partition of the data without explicitly defining an inner-product operator to divide the space, and organized the partition results in a pseudometric based partition tree. By repeatedly applying the triangular inequality, a fast closest-pair searching algorithm was developed within the ESPRIT-Tree framework. An efficient method for dynamic insertion and deletion of tree nodes were also developed.