Economics at your fingertips  

Understanding and reducing variability of SOM neighbourhood structure

Patrick Rousset, Christiane Guinot and Bertrand Maillet
Additional contact information
Patrick Rousset: CEREQ - Centre d'études et de recherches sur les qualifications - ministère de l'Emploi, cohésion sociale et logement - M.E.N.E.S.R. - Ministère de l'Éducation nationale, de l’Enseignement supérieur et de la Recherche

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

Abstract: The self-organizing map (SOM) is a nonlinear unsupervised method for vector quantization. In the context of classification and data analysis, the SOM technique highlights the neighbourhood structure between clusters. The correspondence between this clustering and the input proximity is called the topology preservation. We present here a stochastic method based on bootstrapping in order to increase the reliability of the induced neighbourhood structure. Considering the property of topology preservation, a local approach of variability (at an individual level) is preferred to a global one. The resulting (robust) map, called R-map, is more stable relatively to the choice of the sampling method and to the learning options of the SOM algorithm (initialization and order of data presentation). The method consists of selecting one map from a group of several solutions resulting from the same self-organizing map algorithm, but obtained with various inputs. The R-map can be thought of as the map, among the group of solutions, corresponding to the most common interpretation of the data set structure. The R-map is then the representative of a given SOM network, and the R-map ability to adjust the data structure indicates the relevance of the chosen network.

Keywords: Self-organizing maps; Robustness; Reliability; Bootstrap; Neighbourhood; Variability; R-map (search for similar items in EconPapers)
Date: 2006
Note: View the original document on HAL open archive server:
References: Add references at CitEc
Citations Track citations by RSS feed

Published in Neural Networks, Elsevier, 2006, 19 (6-7), pp.838-846. 〈10.1016/j.neunet.2006.05.017〉

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL
Series data maintained by CCSD ().

Page updated 2017-10-22
Handle: RePEc:hal:cesptp:hal-00308977