Handling uncertainty in clustering art-exhibition visiting styles

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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.




Gullo, F., Ponti, G., Tagarelli, A., Cuomo, S., De Michele, P., & Piccialli, F. (2017). Handling uncertainty in clustering art-exhibition visiting styles. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 194 LNICST, pp. 54–63). Springer Verlag. https://doi.org/10.1007/978-3-319-58967-1_7

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