Clustering functional data with the SOM algorithm

Abstract. In many situations, high dimensional data can be considered as sampled functions. We show in this paper how to implement a Self-Organizing Map (SOM) on such data by approximating a theoretical SOM on functions thanks to basis expansion. We illustrate the proposed method on real world spectrometric data for which functional preprocessing is very successful.

• Type: Conference
• Year: 2004
• Where: ESANN
• Authors: Fabrice Rossi, Brieuc Conan-Guez, Aïcha El Golli
• Keywords: ESANN 2004, ESANN 2007, Functions Thanks, Theoretical Som, World Spectrometric Data