Looking for Archetypes: Applying Game Data Mining to Hearthstone Decks Mora García, Antonio Miguel Fernández Ares, Antonio Javier García Sánchez, Pablo Video games Hearthstone Archetypes Collectible Card Games Artificial intelligence Game Data Mining Data visualisation Clustering Techniques Inteligencia artificial Digital Collectible Cards Games such as Hearthstone have become a very proli c test-bed for Arti cial Intelligence algorithms. The main researches have focused on the implementation of autonomous agents (bots) able to effectively play the game. However, this environment is also very attractive for the use of Data Mining (DM) and Machine Learning (ML) techniques, for analysing and extracting useful knowledge from game data. The objective of this work is to apply existing Game Mining techniques in order to study more than 600,000 real decks (groups of cards) created by players with many di erent skill levels. Data visualisation and analysis tools have been applied, namely, Graph representations and Clustering techniques. Then, an expert player has conducted a deep analysis of the results yielded by these methods, aiming to identify the use of standard - and well-known - archetypes de ned by the players. The used methods will also make it possible for the expert to discover hidden relationships between cards that could lead to nding better combinations of them, enhancing players' decks or, otherwise, identify unbalanced cards that could lead to a disappointing game experience. Moreover, although this work is mostly focused on data analysis and visualization, the obtained results can be applied to improve Hearthstone Bots' behaviour, e.g. predicting opponent's actions after identifying a speci c archetype in his/her deck. 2022-11-17T09:28:34Z 2022-11-17T09:28:34Z 2022-10-17 journal article Antonio M Mora... [et al.]. Looking for archetypes: Applying game data mining to hearthstone decks. Entertainment Computing, Elsevier, 2022, 43, pp.100498. [10.1016/j.entcom.2022.100498]. hal-03815494 https://hdl.handle.net/10481/78011 10.1016/j.entcom.2022.100498 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier