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. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.
|Titolo:||Handling uncertainty in clustering art-exhibition visiting styles|
|Data di pubblicazione:||2017|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|