Coupling clustering and visualization for knowledge discovery from data
Guénaël Cabanès, Younès Bennani · 2011
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. One solution commonly proposed is the use of a condensed description of the properties and structure of data. Thus, it becomes crucial to have visualization tools capable of representing the data structure, not from the data themselves, but from these condensed descriptions. The purpose of our work described in this paper is to develop and put a synergistic visualization of data and knowledge into the knowledge discovery process. We propose here a method of describing data from enriched and segmented prototypes using a 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 method.