Fuzzy growing hierarchical self-organizing networks

Barreto-Sanz, Miguel (Institut des Systèmes d'Information (ISI), Université de Lausanne (UNIL), Hautes etudes Commerciales (HEC), Lausanne, Switzerland ; School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland ; Corporación Biotec, Palmira, Valle del Cauca, Colombia) ; Pérez-Uribe, Andrés (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Peña-Reyes, Carlos Andrés (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Tomassini, Marco (Institut des Systèmes d'Information (ISI), Université de Lausanne (UNIL), Hautes Etudes Commerciales (HEC), Lausanne, Switzerland)

Hierarchical Self-Organizing Networks are used to reveal the topology and structure of datasets. Those structures create crisp partitions of the dataset producing branches or prototype vectors that represent groups of data with similar characteristics. However, when observations can be represented by several prototypes with similar accuracy, crisp partitions are forced to classify it in just one group, so crisp divisions usually lose information about the real dataset structure. To deal with this challenge we propose the Fuzzy Growing Hierarchical Self-Organizing Networks (FGHSON). FGHSON are adaptive networks which are able to reflect the underlying structure of the dataset, in a hierarchical fuzzy way. These networks grow by using three variables which govern the membership degree of data observations to its prototype vectors and the quality of the network representation. The resulting structure allows to represent heterogeneous groups and those that present similar membership degree to several clusters.


Keywords:
Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
IICT - Institut des Technologies de l'Information et de la Communication
Publisher:
Prague, Czech Republic, 3-6 September 2008
Date:
2008-09
Prague, Czech Republic
3-6 September 2008
Berlin, Heidelberg
Springer
Pagination:
10 p.
Published in:
Proceedings of International Conference on Artificial Neural Networks (ICANN 2008), 3-6 September 2008, Prague, Czech Republic ; Lecture Notes in Computer Science
Numeration (vol. no.):
2008, vol. 5164, part 2, pp. 713-722
DOI:
ISSN:
0302-9743
ISBN:
978-3-540-87558-1
Appears in Collection:



 Record created 2020-02-25, last modified 2020-02-25


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