Note: Most tree growing methods favor greatest impurity reduction near the root node.
Ex.
To assign category to a leaf node.
Easy!
If sample data is pure
-> assign this class to leaf.
else
-> assign the most frequent class.
Note: Problem of building decision tree is "ill-conditioned"
i.e. small variance in the training data can yield large variations in decision rules obtained.
Ex. p.405(D&H)
A small move of one sample data can change the decision rules a lot.
Reference about clustering
"Data clustering, a review," A.K. Jain, M.N. Murty, P.J. Flynn
"Algorithms for clustering data," A.K. Jain, R.C. Dibes[1]
"Support vector clustering," Ben-Hur, Horn, Siegelmann, Vapnik [2]
"Dynamic cluster formation using level set methods," Yip, Ding, Chan[3]
What is clustering?
The task of finding "natural " groupings in a data set.
Synonymons="unsupervised learning"
