ADAN is an Artificial Intelligence software product which allows unsupervised data structuring.

It was created by the R+D team at Aplicaciones en Informática Avanzada, and constitutes an innovative contribution in terms of assistance with decision-making processes, by transforming information into knowledge.

ADAN is an automatic learning tool which carries out conceptual data grouping. It performs autonomous extraction of the underlying structure of a data base by similarity grouping of the various items in relation to relevant variables.

'Clustering' analysis constitutes an important technique in the field of initial data analysis. It organises data by abstraction of the underlying structure, grouping individuals into a 'cluster' hierarchy with none of the prior or subjective suppositions commonly found within most other statistical methods.

This process is known as Unsupervised Learning in recognition of Artificial Intelligence literature and trends. One crucial factor which makes a distinction between clustering analysis of recognition of trends, decision analysis and discriminating analysis is that no prior definition of category labels or identifiers is required. The objective of the clustering algorithms is to recognise structures by making use of the data. There are many more advantages to clustering techniques. Clusters provide an impartial view of the data's structure, in a fraction of the time required for manual grouping operations, leaving users free to spend more time on analysis of the results obtained by the product.