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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
We used the SSJ library, available at http://www.iro.umontreal.ca/∼simardr/ssj/
- 3.
Experiments were conducted on an ENEA server of CRESCO4 HPC cluster hosted in Portici [12] – http://www.cresco.enea.it/
References
Aggarwal, C.C.: Managing and Mining Uncertain Data: Advances in Database Systems, vol. 35. Kluwer, Boston (2009). http://dx.doi.org/10.1007/978-0-387-09690-2
Bello-Orgaz, G., Jung, J., Camacho, D.: Social big data: recent achievements and new challenges. Inf. Fusion 28, 45–59 (2016)
Chau, M., Cheng, R., Kao, B., Ng, J.: Uncertain data mining: an example in clustering location data. In: Proceedings of PAKDD Conference, pp. 199–204 (2006)
Chianese, A., Marulli, F., Piccialli, F., Benedusi, P., Jung, J.: An associative engines based approach supporting collaborative analytics in the internet of cultural things. Future Gener. Comput. Syst. 66, 187–198 (2016)
Chianese, A., Piccialli, F.: Improving user experience of cultural environment through IoT: the beauty or the truth case study. Smart Innov. Syst. Technol. 40, 11–20 (2015)
Chianese, A., Piccialli, F., Riccio, G.: Designing a smart multisensor framework based on Beaglebone Black Board. In: Park, J., Stojmenovic, I., Jeong, H., Yi, G. (eds.) Computer Science and its Applications. LNEE, vol. 330, pp. 391–397. Springer, Heidelberg (2015)
Cuomo, S., De Michele, P., Galletti, A., Piccialli, F.: A cultural heritage case study of visitor experiences shared on a social network, pp. 539–544 (2015)
Cuomo, S., De Michele, P., Galletti, A., Pane, F., Ponti, G.: Visitor dynamics in a cultural heritage scenario. In: DATA 2015 - Proceedings of 4th International Conference on Data Management Technologies and Applications, Colmar, Alsace, France, 20–22 July 2015, pp. 337–343 (2015). http://dx.doi.org/10.5220/0005579603370343
Cuomo, S., De Michele, P., Galletti, A., Ponti, G.: Visiting styles in an art exhibition supported by a digital fruition system. In: 11th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2015, Bangkok, Thailand, 23–27 November 2015, pp. 775–781 (2015). http://dx.doi.org/10.1109/SITIS.2015.87
Cuomo, S., De Michele, P., Galletti, A., Ponti, G.: Classify visitor behaviours in a cultural heritage exhibition. In: Helfert, M., Holzinger, A., Belo, O., Francalanci, C. (eds.) DATA 2015. CCIS, vol. 584, pp. 17–28. Springer, Cham (2016). doi:10.1007/978-3-319-30162-4_2
Cuomo, S., De Michele, P., Galletti, A., Ponti, G.: Influence of some parameters on visiting style classification in a cultural heritage case study. In: Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds.) Intelligent Interactive Multimedia Systems and Services 2016. Smart Innovation, Systems and Technologies, vol. 55, pp. 567–576. Springer, Cham (2016). http://dx.doi.org/10.1007/978-3-319-39345-2_50. iIMSS - IS07: Internet of Things: Architecture, Technologies and Applications Invited Session of KES 2016
Ponti, G., et al.: The role of medium size facilities in the HPC ecosystem: the case of the new CRESCO4 cluster integrated in the ENEAGRID infrastructure. In: International Conference on High Performance Computing and Simulation, HPCS 2014, Bologna, Italy, 21–25 July 2014, pp. 1030–1033 (2014)
Gullo, F., Ponti, G., Tagarelli, A.: Clustering uncertain data via K-medoids. In: Greco, S., Lukasiewicz, T. (eds.) SUM 2008. LNCS, vol. 5291, pp. 229–242. Springer, Heidelberg (2008). doi:10.1007/978-3-540-87993-0_19
Gullo, F., Ponti, G., Tagarelli, A.: Minimizing the variance of cluster mixture models for clustering uncertain objects. In: Proceedings of IEEE ICDM Conference, pp. 839–844 (2010)
Gullo, F., Ponti, G., Tagarelli, A.: Minimizing the variance of cluster mixture models for clustering uncertain objects. Stat. Anal. Data Min. 6(2), 116–135 (2013)
Gullo, F., Tagarelli, A.: Uncertain centroid based partitional clustering of uncertain data. PVLDB 5(7), 610–621 (2012)
Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice-Hall, Upper Saddle River (1988)
Jiang, B., Pei, J., Tao, Y., Lin, X.: Clustering uncertain data based on probability distribution similarity. IEEE Trans. Knowl. Data Eng. 25(4), 751–763 (2013)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
van Rijsbergen, C.J.: Information Retrieval. Butterworths, London (1979)
Sarma, A.D., Benjelloun, O., Halevy, A.Y., Nabar, S.U., Widom, J.: Representing uncertain data: models, properties, and algorithms. VLDB J. 18(5), 989–1019 (2009). doi:10.1007/s00778-009-0147-0
Veron, E., Levasseur, M., Barbier-Bouvet, J.: Ethnographie de l’exposition. Paris, Biblioth`eque Publique d’Information, Centre Georges Pompidou (1983)
Zancanaro, M., Kuflik, T., Boger, Z., Goren-Bar, D., Goldwasser, D.: Analyzing museum visitors’ behavior patterns. In: Proceedings of 11th International Conference on User Modeling 2007, UM 2007, Corfu, Greece, 25–29 June 2007, pp. 238–246 (2007)
Züfle, A., Emrich, T., Schmid, K.A., Mamoulis, N., Zimek, A., Renz, M.: Representative clustering of uncertain data. In: Proceedings of KDD Conference, pp. 243–252 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Gullo, F., Ponti, G., Tagarelli, A., Cuomo, S., De Michele, P., Piccialli, F. (2017). Handling Uncertainty in Clustering Art-Exhibition Visiting Styles. In: Jung, J., Kim, P. (eds) Big Data Technologies and Applications. BDTA 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 194. Springer, Cham. https://doi.org/10.1007/978-3-319-58967-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-58967-1_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-58966-4
Online ISBN: 978-3-319-58967-1
eBook Packages: Computer ScienceComputer Science (R0)