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Authors: Salvatore Cuomo 1 ; Pasquale De Michele 1 ; Giovanni Ponti 2 and Maria Rosaria Posteraro 1

Affiliations: 1 University of Naples Federico II, Italy ; 2 ENEA Portici Research Center, Italy

Keyword(s): Computational Neural Models, Clustering, Data Mining, User Profiling.

Abstract: We propose a biologically inspired mathematical model to simulate the personalized interactions of users with cultural heritage objects. The main idea is to measure the interests of a spectator w.r.t. an artwork by means of a model able to describe the behaviour dynamics. In this approach, the user is assimilated to a computational neuron, and its interests are deduced by counting potential spike trains, generated by external currents. The main novelty of our approach consists in resorting to clustering task to discover natural groups, which are used in the next step to verify the neuronal response and to tune the computational model. Preliminary experimental results, based on a phantom database and obtained from a real world scenario, are shown. To discuss the obtained results, we report a comparison between the cluster memberships and the spike generation; our approach resulted to perfectly model cluster assignment and spike emission.

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Paper citation in several formats:
Cuomo, S.; De Michele, P.; Ponti, G. and Posteraro, M. (2014). A Clustering-based Approach for a Finest Biological Model Generation Describing Visitor Behaviours in a Cultural Heritage Scenario. In Proceedings of 3rd International Conference on Data Management Technologies and Applications (DATA 2014) - KomIS; ISBN 978-989-758-035-2; ISSN 2184-285X, SciTePress, pages 427-433. DOI: 10.5220/0005144104270433

@conference{komis14,
author={Salvatore Cuomo. and Pasquale {De Michele}. and Giovanni Ponti. and Maria Rosaria Posteraro.},
title={A Clustering-based Approach for a Finest Biological Model Generation Describing Visitor Behaviours in a Cultural Heritage Scenario},
booktitle={Proceedings of 3rd International Conference on Data Management Technologies and Applications (DATA 2014) - KomIS},
year={2014},
pages={427-433},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005144104270433},
isbn={978-989-758-035-2},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of 3rd International Conference on Data Management Technologies and Applications (DATA 2014) - KomIS
TI - A Clustering-based Approach for a Finest Biological Model Generation Describing Visitor Behaviours in a Cultural Heritage Scenario
SN - 978-989-758-035-2
IS - 2184-285X
AU - Cuomo, S.
AU - De Michele, P.
AU - Ponti, G.
AU - Posteraro, M.
PY - 2014
SP - 427
EP - 433
DO - 10.5220/0005144104270433
PB - SciTePress

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