Design and Implementation of an Intelligent Mobile Game

Full Text (PDF, 561KB), PP.10-16

Views: 0 Downloads: 0


Ekin Ekinci 1,* Fidan Kaya Gulagiz 1 Sevinc iihan Omurca 1

1. Kocaeli University / Computer Engineering Department, Kocaeli, 41000, Turkey

* Corresponding author.


Received: 10 Dec. 2016 / Revised: 9 Jan. 2017 / Accepted: 6 Feb. 2017 / Published: 8 Mar. 2017

Index Terms

Mobile game, K-means, Iterative K-means, Iterative Multi K-means, K-medoids


While the mobile game industry is growing with each passing day with the popularization of 3G smart devices, the creation of successful games, which may interest users, become quite important in terms of the survival of the designed game. Clustering, which has many application fields, is a successful method and its implementation in the field of mobile games is inevitable. In this study, classical ball blasting game was carried out based on clustering. In the game, clustering the color codes with K-means, Iterative K-means, Iterative Multi K-means and K-medoids methods and blasting the balls of colors located in the same cluster by bringing them together were proposed. As a result of the experiments, the suitability of clustering methods for mobile based ball blasting game was shown. At the same time, the clustering methods were compared to produce the more successful clusters and because of obtaining more accurate results and stability, the use of K-medoids method has been chosen for this game.

Cite This Paper

Ekin Ekinci, Fidan Kaya Gülağız, Sevinç İlhan Omurca, "Design and Implementation of an Intelligent Mobile Game", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.3, pp.10-16, 2017. DOI:10.5815/ijmecs.2017.03.02


[1]Yang, B., Zhang, Z.: Design and Implementation of High Performance Mobile Game on Embedded Device. Proceedings of the International Conference on Computer Application and System Modeling, Taiyuan, China, (2010).
[2]K. Klinger, Mobile Computing: Concepts, Methodologies, Tools, and Applications, 1st ed. IGI Global, 2009.
[3]Newzoo Company. Link: d-reports/the-2015-gmgc-global-mobile-games-industrywh itebook/ (Last Access Date: 02.08.2016).
[4]Trisnadoli, A., Hendradjaya, B., Sunindyo, WD.: A Proposal of Quality Model for Mobile Game. Proceedings of the 5th International Conference on Electrical Engineering and Informatics, Bali, Indonesia, (2015).
[5]N. Doğru, A. Subaşı, “A Comparision of Clustering Techniques for Traffic Accident Detection,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 23, pp. 2124-2137, 2015.
[6]J. H. Chen, W. L. Hung, “An Automatic Clustering Algorithm for Probability Density Functions,” Journal of Statistical Computation and Simulation, vol. 85, no. 15, pp. 304-3063, 2015.
[7]Bijuraj, L.V.: Clustering and Its Applications. Proceedings of the National Conference on New Horizons in IT, Mumbai, India, (2013).
[8]A.S. Shirkhorshidi, S. Aghabozorgi, T.Y. Wah, “A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data,” Plos One Journal, vol. 10, no. 12, 2015.
[9]A. K. Jain, M.N. Murty, P. J. Flynn, “Data Clustering: a Review,” ACM Computing Surveys, vol. 31, no. 3, 1999.
[10]P.Jain, B. Buksh, “Accelerated K-means Clustering Algorithm,” International Journal of Information Technology and Computer Science, vol. 8, no. 10, 2016.
[11]S. Chittineni, R. B. Bhogapathi, “Determining Contribution of Features in Clustering Multidimensional Data Using Neural Network,” International Journal of Information Technology and Computer Science, vol. 4, no. 10, 2012.
[12]S. G. Rao, A. Govardhan, “Evaluation of H- and G-indices of Scientific Authors using Modified K-Means Clustering Algorithm,” International Journal of Information Technology and Computer Science, vol. 8, no. 2, 2016.
[13]Y. Y. Shih, C. Y., Liu, “A method for customer lifetime value ranking — Combining the analytic hierarchy process and clustering analysis,” Journal of Database Marketing & Customer Strategy Management, vol. 11, no. 2, 2003.
[14]MacQueen, J.: Some Methods for Classification and Analysis of Multivariate Observation. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, California, USA, (1967).
[15]Kaya, H., Köymen, K.: Veri Madenciliği Kavrami ve Uygulama Alanlari. Doğu Anadolu Bölgesi Araştırmaları, (2008).
[16]L. Kaufman, P.J. Rousseeuw, Statistical Data Analysis Based on the L1–Norm and Related Methods, Springer, 2002.
[17]L. Kaufman, P.J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, 1990.
[18]M. Işık, A. Çanurcu, “K-ortalamalar, K-ortancalar ve Bulanik C-Means Algoritmalarının Uygulamalı Olarak Performanslarının Tespiti,” İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, vol. 6, no. 11, 2007.
[19]T. Abeel, Y.V. Peer, Y. Saeys, “Java-ML: A Machine Learning Library,” Journal of Machine Learning Research, vol. 10, 2009.