Chapter 24 DB systems textbook

To build a decision tree:
Input : node n, parition D, split selection method S
Output: decision tree for D rooted at node n

Top-Down decision tree induction schema:
BuildTree(Node n, data partition D, split selection method S)
   Apply S to D to find the splitting criterion
   if ( a good splitting criterion is found)
       Create two children nodes n1 and n2 of n
       Partition D into D1 and D2
       BuildTree(n1, D1, S)
       BuildTree(n2, D2, S)
   endif

Clustering:
Input : node n, partition D, split selection method S
Output: decision tree for D rooted at node n

Top-Down decision Tree induction Schema:
BuildTree(Node n, data partition D, split selection method S)
(1a) Make a scan over D and construct the AttributeValue Classlabels (AVC) group of n in-memory.
(1b) Apply S to the AVC group to find the splitting criterion

A partitioning clustering algorithm partitions the data into k groups such that some criterion that evaluates the clustering quality is optimized. k is specified by the user. A hierarchical clustering algorithm generates a sequence of partitions of the records. Starting with a partition in which each cluster consists of one single record, the algorithm merges two partitions in each step until one single partition remains in the end.