Enriched topological learning for cluster detection and visualization

Guénaël Cabanes (1) , Younès Bennani (1) , Dominique Fresneau (2)

1 LIPN - Laboratoire d'Informatique de Paris-Nord
2 LEEC - Laboratoire d'Ethologie Expérimentale et Comparée

The exponential growth of data generates terabytes of very large databases. The growing number of data dimensions and data objects presents tremendous challenges for effective data analysis and data exploration methods and tools. Thus, it becomes crucial to have methods able to construct a condensed description of the properties and structure of data, as well as visualization tools capable of representing the data structure from these condensed descriptions. The purpose of our work described in this paper is to develop a method of describing data from enriched and segmented prototypes using a topological clustering algorithm. We then introduce a visualization tool that can enhance the structure within and between groups in data. We show, using some artificial and real databases, the relevance of the proposed approach.

Mots clés: Self-Organizing Map; Prototypes Enrichment; Two-Level Clustering; Coclustering; Visualization

Domaines: Analyse de données, Statistiques et Probabilités [physics.data-an]

Guénaël Cabanes, Younès Bennani, Dominique Fresneau. Enriched topological learning for cluster detection and visualization. Neural Networks, 2012, 32, pp.186 - 195. ⟨10.1016/j.neunet.2012.02.019⟩. ⟨hal-01461451⟩