Cluster analysis groups similar objects together into clusters.  It’s useful in many different fields: for identifying customer segments or even for studying evolution.

There are 2 main types of clustering: agglomerative and divisive.

  • Agglomerative clustering gradually merges similar items together into bigger and bigger clusters
  • Divisive clustering takes a large cluster and gradually divides it into smaller and smaller clusters

First, a statistical method is chosen to identify the differences between objects. Euclidian distance is the most common, which is the Square root(SUM(first point’s distance to origin – second point’s distance to origin)^2)  Distance is nothing more than the difference between two clusters.  In the marketing data example (if we’re looking at customer ages), distance would be the difference in ages.

 

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