Interpreting cluster analysis results

Interpretation of the clustering structure and the clusters is an essential step in unsupervised learning. Identifying the characteristics that underlie differentiation between groups allows to ensuring their credibility.

In this course material, we explore the univariate and multivariate techniques. The first ones have the merit of the ease of calculation and reading, but do not take into account the joint effect of the variables. The seconds are a priori more efficient, but require additional expertise to fully understand the results.

Keywords: cluster analysis, clustering, unsupervised learning, percentage of variance explained, V-Test, test value, distance between centroids, correlation ratio
Slides: Characterizing the clusters
References:
Tanagra Tutorial, "Understanding the 'test value' criterion", May 2009.
Tanagra Tutorial, "Hierarchical agglomerative clustering", June 2017.
Tanagra Tutorial, "K-Means clustering", June 2017.