The goal of data classification is to organize them into relevant groups using algorithms. In this study, two clustering algorithms are applied to classify a set of geophysical measurements performed around the Lascaux Cave (Dordogne, France). Based on a non-destructive geophysical method, electrical resistivity tomography (ERT), the data (resistivity values) are supposed to characterize the rock mass around the cave. The rock mass must be divided into an optimal number of homogeneous domains with specific thermal properties to integrate them in the future thermo-aeraulic simulations of the cave. Since the data are georeferenced, a given resistivity cluster corresponds to a specific spatial domain of the rock mass. This study aims to compare two different clustering methods, the Hierarchical Agglomerative Clustering (HAC) and the K-means methods, on the resistivity data set. Thus, the objective of this study is to determine which of the two methods leads to a partition of the massif with an optimal number of classes, allowing us to find the geological structures partially known thanks to previous studies. The results of these methods are analyzed in light of two different indices, the Silhouette Index (SI) and the Coefficient of Variation (CV). Based on these indices alone, K-means might seem to be the best algorithm, but a fine analysis of each cluster shows that the HAC method gives better results. Indeed, only the HAC method highlights areas already known to be either waterlogged or clayey. Our partial geological knowledge, which is of paramount importance in such a study, also supports the choice of the HAC method.