[SOUND] In this session,

we examine more detail on divisive

clustering algorithms.

We already introduced a general concept of divisive clustering.

Essentially is, we start from a big single macro-cluster,

and we try to find how to split them into two smaller clusters.

We continue doing this, finally, every single node become a singleton cluster.

Okay.

So, this method, actually described in Kaufmann and

Rousseeuew's 1990 book, called DIANA or Divisive Analysis,

is also implemented in some statistic packages, such as Splus.

This is essentially inverse of AGNES.

That means you start from bigger cluster, you start splitting them continuously,

recursively, finally you'll find that each one is a single, singleton cluster.

Divisive clustering is a top down approach,

because you start from all the points as one cluster, then

you'll recursively split the high level cluster to build the dendogram, okay?