Big Data Technologies and Applications. 7th International Conference, BDTA 2016, Seoul, South Korea, November 17–18, 2016, Proceedings

Research Article

Handling Uncertainty in Clustering Art-Exhibition Visiting Styles

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  • @INPROCEEDINGS{10.1007/978-3-319-58967-1_7,
        author={Francesco Gullo and Giovanni Ponti and Andrea Tagarelli and Salvatore Cuomo and Pasquale Michele and Francesco Piccialli},
        title={Handling Uncertainty in Clustering Art-Exhibition Visiting Styles},
        proceedings={Big Data Technologies and Applications. 7th International Conference, BDTA  2016, Seoul, South Korea, November 17--18, 2016, Proceedings},
        proceedings_a={BDTA},
        year={2017},
        month={6},
        keywords={Uncertain objects Clustering Data mining Cultural heritage data},
        doi={10.1007/978-3-319-58967-1_7}
    }
    
  • Francesco Gullo
    Giovanni Ponti
    Andrea Tagarelli
    Salvatore Cuomo
    Pasquale Michele
    Francesco Piccialli
    Year: 2017
    Handling Uncertainty in Clustering Art-Exhibition Visiting Styles
    BDTA
    Springer
    DOI: 10.1007/978-3-319-58967-1_7
Francesco Gullo1,*, Giovanni Ponti2,*, Andrea Tagarelli3,*, Salvatore Cuomo4,*, Pasquale Michele4,*, Francesco Piccialli4,*
  • 1: UniCredit
  • 2: DTE-ICT-HPC, ENEA Portici
  • 3: University of Calabria
  • 4: University of Naples “Federico II”
*Contact email: gullof@acm.org, giovanni.ponti@enea.it, andrea.tagarelli@unical.it, salvatore.cuomo@unina.it, pasquale.demichele@unina.it, francesco.piccialli@unina.it

Abstract

Uncertainty is one of the most critical aspects that affect the quality of Big Data management and mining methods. Clustering uncertain data has traditionally focused on data coming from location- based services, sensor networks, or error-prone laboratory experiments. In this work we study for the first time the impact of clustering uncertain data on a novel context consisting in visiting styles in an art exhibition. We consider a dataset derived from the interaction of visitors of a museum with a complex Internet of Things (IoT) framework. We model this data as a set of uncertain objects, and cluster them by employing the well-established UK-medoids algorithm. Results show that clustering accuracy is positively impacted when data uncertainty is taken into account.