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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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