October 22, 2012

Hierarchical clustering on Individuals

The data classification is an important thing to extract some interesting information. The internal structure of ur data set is really complex. If we arrive to extract some templates or some forms in our information, we can create some usable groups and study independently each group.
In this study there are a lot of classical methods as k-means, Hierarchical clustering and Self Organized Map.

We start with the Hierarchical clustering analysis on individuals
In this analysis we group data or data group by distance one by one. After a certain time, a result can be extracted easily  And two notices are usable: searching the best jump or by selecting manually a number of classes.
The result of this classification is this screen.

In this result we can see a Hierarchical clustering structure that permir to estimate a minimum four classes.
The results colored in a two first axes Principal Component Analysis is:

No comments:

Post a Comment