Abstract
Video-games industry is specially focused on user entertainment. It is really important for these companies to develop interactive and usable games in order to satisfy their client preferences. The main problem for the game developers is to get information about the user behaviour during the game-play. This information is important, specially nowadays, because gamers can buy new extra levels, or new games, interactively using their own consoles. Developers can use the gamer profile extracted from the game-play to create new levels, adapt the game to different user, recommend new video games and also match up users. This work tries to deal with this problem. Here, we present a new game, called “Dream”, whose philosophy is based on the information extraction process focused on the player game-play profile and its evolution. We also present a methodology based on time series clustering to group users according to their profile evolution. This methodology has been tested with real users which have played Dream during several rounds.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aler, R., Valls, J.M., Camacho, D., Lopez, A.: Programming robosoccer agents by modeling human behavior. Expert Systems with Applications 36(2), 1850–1859 (2009)
Bello-Orgaz, G., Menéndez, H.D., Camacho, D.: Adaptive k-means algorithm for overlapped graph clustering. International Journal of Neural Systems 22(05), 1250018 (2012), PMID: 22916718
Creighton, R.H.: Unity 3D Game Development by Example: A Seat-of-Your-Pants Manual for Building Fun, Groovy Little Games Quickly. Packt Publishing Ltd. (2010)
Ip, B.: Technological, content, and market convergence in the games industry. Games and Culture 3(2), 199–224 (2008)
Jiménez-Dıaz, G., Menéndez, H.D., Camacho, D., González-Calero, P.A.: Predicting performance in team games. In: II for Systems, C. Technologies of Information, and Communication, ICAART, pp. 401–406 (2011)
Kaufman, L., Rousseeuw, P.: Clustering by Means of Medoids. Reports of the Faculty of Mathematics and Informatics. Faculty of Mathematics and Informatics (1987)
Larose, D.T.: Discovering Knowledge in Data. John Wiley & Sons (2005)
Warren Liao, T.: Clustering of time series data – a survey. Pattern Recognition 38(11), 1857–1874 (2005)
Menéndez, H.D., Barrero, D.F., Camacho, D.: A genetic graph-based approach for partitional clustering. International Journal of Neural Systems 24(03),1430008 (2014) PMID: 24552507
Williams, D., Yee, N., Caplan, S.E.: Who plays, how much, and why? debunking the stereotypical gamer profile. Journal of Computer-Mediated Communication 13(4), 993–1018 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Menéndez, H.D., Vindel, R., Camacho, D. (2014). Combining Time Series and Clustering to Extract Gamer Profile Evolution. In: Hwang, D., Jung, J.J., Nguyen, NT. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. Lecture Notes in Computer Science(), vol 8733. Springer, Cham. https://doi.org/10.1007/978-3-319-11289-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-11289-3_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11288-6
Online ISBN: 978-3-319-11289-3
eBook Packages: Computer ScienceComputer Science (R0)