Mimic Visiting Styles by Using a Statistical Approach in a Cultural Event Case Study | Zendy

Salvatore Cuomo | Zendy; Pasquale De Michele | Zendy; Monica Pragliola | Zendy; Gerardo Severino | Zendy

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Mimic Visiting Styles by Using a Statistical Approach in a Cultural Event Case Study

Author(s) -

Salvatore Cuomo,

Pasquale De Michele,

Monica Pragliola,

Gerardo Severino

Publication year - 2016

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2016.09.071

Subject(s) - computer science , event (particle physics) , cluster analysis , style (visual arts) , dynamics (music) , process (computing) , data science , artificial intelligence , human–computer interaction , machine learning , visual arts , psychology , art , pedagogy , physics , quantum mechanics , operating system

Classifying the behaviours of users in the cultural spaces with the aim to infer knowledge about the event fruition is a fascinating challenge. In this paper, starting from real data, we are interested in predicting the user dynamics related to the interaction of a spectator with artworks and with the available technologies. Clustering techniques are preliminary used to find groups that reflect visiting styles. Accordingly with this, we assume that visitors are previously classified. The start-up dynamical process underlying classification can be affected by several errors. Here we adopt a powerful statistical method to predict the visiting style dynamics of spectators. Finally, numerical experiments confirm that it is possible to predict the visitors’ behaviour with good results

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