OK. Uh. Got anything to tell us ?
Um. Well , I 've been reading some literature about clustering of data. Just , um , I guess , let me put it in context. OK , so we 're talking about discovering intermediate categories to , um  to classify. And , uh , I was looking at some of the work that , uh , Sangita was doing on these TRAPS things. So she has , um  she has temporal patterns for , um , a certain set of phonemes , from  from TIMIT , right ? the most common phonemes. And each one of them has  has a  a nice pattern over time , a one  one second window. And it has  has these patterns. Um , so she has , um a TRAP for each one of the phonemes , um , times fifteen , for each of the fifteen critical bands. And , um ,  she does this agglomerative hierarchical clustering which  which basically , um , is a clustering algorithm that , uh , starts with many , many , many different points  many different clusters  uh , corresponding to the number of data , uh , patterns that you have in the data. And then you have this distance mej metric which , uh , measures how  how closely related they are. And you start , um  by merging the patterns that are most closely related.
And you create a tree.
And y yeah , yeah , a dendrogram tree.