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Learning | Clustering | Data Clustering

Data Clustering is a form of unsupervised learning that is utilized to segment the data. The output of the algorithm is a new variable,  [Factor_i]. The states of this new variable correspond to the created segments. 

There are various reasons to use Data Clustering:

  • For finding observations that look the same;
  • For finding observations that behave the same;
  • For representing an unobserved dimension;
  • For compactly representing the joint probability distribution.

From a technical point of view, the segment should be:

  1. Homogeneous/pure;
  2. Have clear differences with the other segments;
  3. Be stable.

From a functional point of view, the segments should be:

  1. Easy to understand;
  2. Operational;
  3. Be a fair representation of the data.


Data Clustering has been updated in versions 5.1 and 5.2.

New Feature: Meta-Clustering

This new feature has been added for improving the stability of the induced solution (3rd technical quality). It consists in using Data Clustering on the data set made of a subset of the Factors that have been created while learning. The final solution is thus a summary of the best solutions that have been found. 


The five variables at the very bottom are called Manifest variables. They are used in the data set for describing the observations.

The Factor variables  [Factor_1],  [Factor_2], and [Factor_3] have been induced with Data Clustering, and then imputed to create new columns in the data set.

In this example, three Factor variables are thus used for creating the final solution [Factor_4].


New Feature: Multinet

New Feature: Heterogeneity

New Feature: Random Weights