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Submitted
June 17, 2024
Accepted
July 1, 2024
Published
July 4, 2024
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Abstract

Chess is a game that requires a high level of intelligence and strategy. Generally, in order to understand complex move patterns and strategies, the expertise of chess masters is required. With the rapid development in the field of machine learning, the digitization of chess game recordings in Portable Game Notation (PGN) format, and the availability of large and widely accessible data, it is possible to apply machine learning techniques to analyze chess games. This research studies the use of text clustering algorithms, specifically hierarchical clustering and K-means clustering, to categorize chess games based on their moves. We extracted 100 chess games that use certain openings such as French Defence, Queen's Gambit Declined, and English Opening. In the implementation of hierarchical clustering, single, average, and complete linkage methods are used. As a result, our findings show that hierarchical clustering with single linkage is less effective. On the other hand, the average and complete linkage methods, as well as K-means clustering, successfully identify clusters corresponding to the original openings. Notably, K-means clustering showed the highest accuracy in clustering chess games. This research highlights the potential of machine learning techniques in uncovering strategic patterns in chess games, paving the way for deeper insights into game strategies.

Keywords

clustering analysis text analysis hierarchical clustering k-means

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Copyright (c) 2024 Feri Wijayanto

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Wijayanto, F. (2024). Clustering Analysis of Chess Portable Game Notation Text. Jurnal Sains, Nalar, Dan Aplikasi Teknologi Informasi, 3(3), 137–142. https://doi.org/10.20885/snati.v3.i3.42
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References

  1. Tim Mann's Chess Pages, "PGN: Portable Game Notation," https://tim-mann.org/Standard. Accessed: Jun. 10, 2024.
  2. R. Janani and S. Vijayarani, "Text document clustering using Spectral Clustering algorithm with Particle Swarm Optimization," Expert Systems with Applications, vol. 134, pp. 192-200, 2019. https://doi.org/10.1016/j.eswa.2019.05.030.
  3. H. Kim, H. K. Kim, and S. Cho, "Improving spherical k-means for document clustering: Fast initialization, sparse centroid projection, and efficient cluster labeling," Expert Systems with Applications, vol. 150, p. 113288, 2020. https://doi.org/10.1016/j.eswa.2020.113288.
  4. H. Rehioui and A. Idrissi, "New clustering algorithms for twitter sentiment analysis," IEEE Systems Journal, vol. 14, no. 1, pp. 530-537, 2019. https://doi.org/10.1109/JSYST.2019.2912759
  5. S. Riaz, M. Fatima, M. Kamran, and M. W. Nisar, "Opinion mining on large scale data using sentiment analysis and k-means clustering," Cluster Computing, vol. 22, pp. 7149-7164, 2019. https://doi.org/10.1007/s10586-017-1077-z
  6. I. Manipur, I. Granata, L. Maddalena, and M. R. Guarracino, "Clustering analysis of tumor metabolic networks," BMC Bioinformatics, vol. 21, pp. 1-14, 2020. https://doi.org/10.1186/s12859-020-03564-9
  7. M. Patel, H. Pandey, T. Wagh, A. D. Hujare, and R. Dangi, "Vecma: An advance chess engine," in 2022 IEEE Pune Section International Conference (PuneCon), 2022, pp. 1-6.
  8. H. Zhang and T. Yu, "AlphaZero," in Deep Reinforcement Learning, H. Dong, Z. Ding, and S. Zhang, Eds. Singapore: Springer, 2020. doi: 10.1007/978-981-15-4095-0_15.
  9. R. McIlroy-Young, R. Wang, S. Sen, J. Kleinberg, and A. Anderson, "Learning models of individual behavior in chess," in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 1253-1263. https://doi.org/10.1145/3534678.3539367
  10. Raghav, Kuldeep, and Laxmi Ahuja. "Chess Opening Analysis Using DBSCAN Clustering and Predictive Modeling." In 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), pp. 1-5. IEEE, 2024.
  11. G. Gan, C. Ma, and J. Wu, Data Clustering: Theory, Algorithms, and Applications. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2020.
  12. A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and J. Heming, "K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data," Information Sciences, vol. 622, pp. 178-210, 2023. https://doi.org/10.1016/j.ins.2022.11.139
  13. A. Subayu, “Penerapan Metode K-Means Untuk Analisis Stunting Gizi Pada Balita: Systematic Review”, SNATI, vol. 2, no. 1, pp. 42–50, Jul. 2022.
  14. M. Meyer and K. Buchta, "proxy: Distance and Similarity Measures," R package version 0.4-27, Comprehensive R Archive Network (CRAN), 2023. [Online]. Available: https://cran.r-project.org/web/packages/proxy/. [Accessed: Jun. 10, 2024].
  15. I. Pauletic, L. N. Prskalo and M. B. Bakaric, "An Overview of Clustering Models with an Application to Document Clustering," 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 2019, pp. 1659-1664, https://doi.org/ 10.23919/MIPRO.2019.8756868.
  16. M. Babaei, S. M. Muyeen and S. Islam, "Identification of Coherent Generators by Support Vector Clustering With an Embedding Strategy," in IEEE Access, vol. 7, pp. 105420-105431, 2019, https://doi.org/10.1109/ACCESS.2019.2932194.