Chess Opening Analysis Using DBSCAN Clustering and Predictive Modeling

This document serves as a live template for scholarly research on the topic “Chess Openings Characterization through DBSCAN Clustering and Predictive Modeling.” In this study we implemented the Density Based Spatial Clustering of Application with Noise (DBSCAN) algorithm on standardized player ratings and one-hot encoded opening moves used by different players. The clusters are labeled based on strategic similarities. Afterwards a prediction model is built using cluster labels along with other features examining how their results can influence the game outcome and thus understanding how opening patterns affect the overall game. The findings from this study makean important contribution to strategic decision-making and the field of chess, demonstrating the potential of DBSCAN in game analytics.