Explain initialization of cluster tree in GRGPF algorithm.

Initialization of cluster tree in GRGPF algorithm :-

• The clusters are organized into a tree, and the nodes of the tree may be very large, perhaps disk blocks or pages, as in the case of a B-tree, which the cluster-representing tree resembles.

• Each leaf of the tree holds as many cluster representations as can fit.

• A cluster representation has a size that does not depend on the number of points in the cluster.

• An interior node of the cluster tree holds a sample of the clustroids of the clusters represented by each of its subtrees, along with pointers to the roots of those subtrees.

• The samples are of fixed size, so the number of children that an interior node may have is independent of its level.

• As we go up the tree, the probability that a given cluster's clustroid is part of the sample diminishes.

• We initialize the cluster tree by taking a main-memory sample of the dataset and clustering it hierarchically.

• The result of this clustering is a tree T, but T is not exactly the tree used by the GRGPF Algorithm. Rather, we select from T certain of its nodes that represent clusters of approximately some desired size n.

• These are the initial clusters for the GRGPF Algorithm, and we place their representations at the leaf of the cluster-representing tree. We then group clusters with a common ancestor in T into interior nodes of the cluster-representing tree. In some cases, rebalancing of the cluster-representing tree will be necessary.