Self-Organizing Maps for imprecise data

Authors:
Pierpaolo Durso;Livia De Giovanni;Riccardo Massari
Affiliations:
Dipartimento di Scienze Sociali ed Economiche, Sapienza University of Rome, P.za Aldo Moro, 5-00185 Rome, Italy;Dipartimento di Scienze Politiche, LUISS Guido Carli, Viale Romania, 32-00197 Rome, Italy;Dipartimento di Scienze Sociali ed Economiche, Sapienza University of Rome, P.za Aldo Moro, 5-00185 Rome, Italy
Venue:
Fuzzy Sets and Systems
Year:
2014

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Abstract

Self-Organizing Maps (SOMs) consist of a set of neurons arranged in such a way that there are neighbourhood relationships among neurons. Following an unsupervised learning procedure, the input space is divided into regions with common nearest neuron (vector quantization), allowing clustering of the input vectors. In this paper, we propose an extension of the SOMs for data imprecisely observed (Self-Organizing Maps for imprecise data, SOMs-ID). The learning algorithm is based on two distances for imprecise data. In order to illustrate the main features and to compare the performances of the proposed method, we provide a simulation study and different substantive applications.