Handling uncertainty in clustering art-exhibition visiting styles

Francesco Gullo, Giovanni Ponti, Andrea Tagarelli, Salvatore Cuomo, Pasquale De Michele, Francesco Piccialli

Research output: Contribution to conferencePaper

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.
Original languageEnglish
DOIs
Publication statusPublished - 2017
Event7th International Conference on Big Data Technologies and Applications, BDTA 2016 - Seoul, Korea, Republic of
Duration: 1 Jan 2017 → …

Conference

Conference7th International Conference on Big Data Technologies and Applications, BDTA 2016
CountryKorea, Republic of
CitySeoul
Period1/1/17 → …

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Cite this

Gullo, F., Ponti, G., Tagarelli, A., Cuomo, S., De Michele, P., & Piccialli, F. (2017). Handling uncertainty in clustering art-exhibition visiting styles. Paper presented at 7th International Conference on Big Data Technologies and Applications, BDTA 2016, Seoul, Korea, Republic of. https://doi.org/10.1007/978-3-319-58967-1_7