In　order　to　solve　the　problem　that　Traditional　Hierarchical　Agglomerative　Clustering　Algorithms　(HACA)　may　produce　a　nonunique　binary　tree　as　the　clustering　result　of　a　same　dataset,　this　paper　presents　Hierarchical　Subtrees　Agglomerative　Clustering　Algorithm　(HSACA),　the　basic　idea　of　which　is　to　find　maximal　θ-distant　subtrees　in　a　minimal　spanning　tree　of　the　data　set　and　merge　its　vertex　set.　HSACA　can　merge　many　objects　into　a　cluster　in　each　step,　and　its　clustering　result　is　usually　a　multiple　tree.　This　paper　proves　in　theory　that　the　multiple　tree　generated　by　HSACA　is　unique　for　a　dataset　without　considering　the　branchy　orders,　and　shows　in　computer　simulations　that　the　multiple　tree　describes　a　more　reasonable　clustering　result　than　the　binary　tree　generated　by　traditional　HACA　if　there　are　many　equidistant　pairs　of　points　in　the　data　set.