Unsupervised Segmentation Methodology

ADAN is an artificial intelligence-based tool that automatically extracts the underlying database structure. It addresses the problem of grouping database elements into groups or clusters based on their similarities. It´s designed for bulk data analysis. Through innovative techniques, ADAN allows experts to analyze the available information for decision making support.

Differential Value

ADAN clustering is a conceptual tool based on unsupervised learning techniques. It uses a powerful exclusive polythetic algorithm, unsupervised, and fully developed by Grupo AIA. The system allows segmentation completion without the constraints of statistical techniques. It includes topological recognition, a contextual explanatory component, and graphical projection of the resulting spatial clusters, among other remarkable features.

Main features

  • Clustering description option for conceptualization (Labeling).
  • Automatic statistical sample selection to expedite the subsequent clustering process.
  • Basic Euclidean metric option using supervised learning techniques.
  • Further refinement of group results by defining a group merge as a new universe.
  • Automatic scaling range of each variable, detecting relevant discrete values.
  • Quality control elements of the process: quality index and spatial representation of the data.
  • It addresses the problem of grouping the elements of a database into groups or clusters based on their similarities.
  • Uses unsupervised learning techniques.
  • Polythetic clustering algorithm (a set of variables chosen and taken simultaneously.
  • The advanced internal algorithm allows unsupervised and automatic selection of the number of end groups.
  • A hierarchical tree graphical representation of the results.


ADAN, designed as a general-purpose analysis tool, has been used for a variety of business applications: company´s growth strategies definition, branches network financing investment, marketing and sales strategies definition to classify data for customers segmentation.

Banks have used it for customers risk analysis and corporations for branches and product quality control. It has also been used by internet surfers and virtual stores customers.

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