Data and text mining with hierarchical clustering ants

Hanene Azzag (1) , Christiane Guinot (2) , Gilles Venturini (3)

1 LIPN - Laboratoire d'Informatique de Paris-Nord
2 CERIES - CENTRE DE RECHERCHES ET D'INVESTIGATIONS EPIDERMIQUES ET SENSORIELLES
3 LIFAT - Laboratoire d'Informatique Fondamentale et Appliquée de Tours

In this paper is presented a new model for data clustering, which is inspired from the self-assembly behavior of real ants. Real ants can build complex structures by connecting themselves to each others. It is shown in this paper that this behavior can be used to build a hierarchical tree-structured partitioning of the data according to the similarities between those data. Several algorithms that may use or not global or local thresholds. We have also introduce an incremental version of our artificial ants algorithm. Those algorithms have bee evaluated using artificial and real databases. Our algorithms obtain competitive results chen compared to the Kmeans, Ascending Hierarchical CLustering, AntClust (two biomimetic methods). Our methods have been applied to three real word applications: the analysis of human healthy skin, the on-line mining of websites usage, and the automatic construction of portal sites. Finally, we have developed two possibilities to explore the portal site. The first possibility consists in representing the tree in HTML pages in order to explore the portal site with a conventional browser. The second possibility we have studied is to integrate the results of our algorithms in our virtual reality data mining tool VRMiner.

Domaines: Interface homme-machine [cs.HC]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Hanene Azzag, Christiane Guinot, Gilles Venturini. Data and text mining with hierarchical clustering ants. A. Abraham, C. Grosan and V. Ramos. Swarm intelligence in data mining, Springer SCI series, pp.153-190, 2006. ⟨hal-01067291⟩