JACIII Vol.12 No.2 pp. 150-155
doi: 10.20965/jaciii.2008.p0150


Haar Wavelets for Online-Game Player Classification with Dynamic Time Warping

Ruck Thawonmas and Keita Iizuka

Intelligent Computer Entertainment Laboratory, Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan

May 14, 2007
September 12, 2007
March 20, 2008
online game, player classification, wavelet transform, dynamic time warping, action sequences
Online game players’ action sequences, while important to understand their behavior, usually contain noise and/or redundancy, making them unnecessarily long. To acquire briefer sequences representative of players’ features, we apply the Haar wavelet transform to action sequences and reconstruct them from selected wavelet coefficients. Results indicate that this approach is effective in classification when the k-nearest neighbor classifier is used to classify players based on dynamic time warping distances between reconstructed sequences.
Cite this article as:
R. Thawonmas and K. Iizuka, “Haar Wavelets for Online-Game Player Classification with Dynamic Time Warping,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.2, pp. 150-155, 2008.
Data files:
  1. [1] R. Bartle, “Hearts, Clubs, Diamonds, Spades: Players Who Suit MUDs,” The Journal of Virtual Environments, Vol.1, No.1, 1996.
  2. [2] R. Thawonmas, J. Y. Ho, and Y. Matsumoto, “User Type Identification in Virtual Worlds,” K. Arai, H. Deguchi, H. Matsui (Eds.), Agent-Based Modeling Meets Gaming Simulation (Post-Proceedings of the Session Conf. of the ISAGA, Int. Simulation and Gaming Association, 2003), Series: Springer Series on Agent Based Social Systems, Vol.2, Springer, pp. 79-88, 2006.
  3. [3] Y. Matsumoto and R. Thawonmas, “MMOG Player Classification Using Hidden Markov Models,” Matthias Rauterberg (Ed.), Lecture Notes in Computer Science, Proc. ICEC 2004, Vol.3166, pp. 429-434, 2004.
  4. [4] L. R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proc. IEEE, Vol.77, No.2, pp. 257-285, 1989.
  5. [5] R. Thawonmas and J. Y. Ho, “Classification of Online Game Players Using Action Transition Probabilities and Kullback Leibler Entropies,” The Journal of Advanced Computational Intelligence and Intelligent Informatics, Special issue on Advances in Intelligent Data Processing, Vol.11, No.3, pp. 319-326, 2007.
  6. [6] K. Chan, A. Fu, and C. Yu, “Haar Wavelets for Efficient Similarity Search of Time-Series: With and Without Time Warping,” IEEE Trans. Knowl. Data Eng., Vol.15, No.3, pp. 686-705, 2003.
  7. [7] S. M. Weiss and C. A. Kulikowski, “Computer Systems That Learn,” Morgan Kaufmann Publishers, San Mateo, CA, 1991.
  8. [8] P. Somervuo, “Online Algorithm for the Self-Organizing Map of Symbol Strings,” Neural Networks, Vol.17, No.8-9, pp. 1231-1239, 2004.
  9. [9] K. Borner and S. Penumarthy, “Social Diffusion Patterns in Three-Dimensional Virtual Worlds,” Information Visualization Journal, Vol.2, No.3, pp. 182-198, 2003.
  10. [10] L. Chittaro, R. Ranon, and L. Ieronutti, “VU-Flow: A Visualization Tool for Analyzing Navigation in Virtual Environments,” IEEE Transactions on Visualization and Computer Graphics, Vol.12, No.6, pp. 1475-1485, 2006.
  11. [11] R. Thawonmas, M. Hirano, and M. Kurashige, “Cellular Automata and Hilditch Thinning for Extraction of User Paths in Online Games,” CD-ROM, Proc. NETGAMES 2006 & 5th Workshop on Network & System Support for Games, 2006.
  12. [12] R. Thawonmas, M. Kurashige, and K. T. Chen, “Detection of Landmarks for Clustering of Online-Game Players,” The Int. Journal of Virtual Reality, Vol.6, No.3, pp. 11-16, 2007.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024