How can I predict euclidean distance ?

I have a data set of 92 variables and their descriptors along with their normalized value and actually I want to apply Kennard and Stone algorithm to divide this data set into two sub set. Please provide me an answer to predict euclidean distance in matlab?

[José-Luis]

What have you tried so far?

[SHAGUN]

I am a beginner and I am trying to apply the mathematical formula for the same.

[José-Luis]

Well, a good way to get answers in this forum is to ask specific Matlab questions, and show what you have done so far, in the form of some code snippet, with data to illustrate your problem. As your question is presently stated, it sounds like you expect us (at least those of us not familiar with Kennard and Stone work) to read some paper, to write the Matlab application, and to give it to you. That sounds an awful lot like work. There are consultants that get paid for that.

[Walter Roberson]

One does not usually "predict" euclidean distance: one normally calculates it.

Answer 1

[Matt J]

The NORM command calculates Euclidean distances.

[Walter Roberson]

or pdist() if you have the Stats toolbox.

[Image Analyst]

I usually use hypot() to give distance, or just do Pythagorean theorem manually. Or use bwdist() if I need it done for every pixel in an image. But I have never heard of Kennard and Stone's algorithm so I don't know what they do or predict.