Archsphere – A cluster algorithm for archaeological applications

Abstract This paper argues that many of the existing cluster algorithms employed by practitioners are too unspecific for archaeological purposes. Based on a large landscape archaeological dataset a cluster algorithm for archaeological applications is developed. It accounts for shortfalls in generic cluster algorithms like the difficulty to cluster point clouds with varying densities in DBSCAN or the absence of a notion of noise in k-means. The application of the Archsphere algorithm is geared towards archaeological problem sets using readily available data from surveys and excavations as input. The introduced method performs the task of spatially dividing an archaeological dataset of monuments into clusters in a more meaningful way than is possible with standard procedures, effectively setting a solid foundation for a scale-consistent landscape archaeological analysis of monument assemblages.

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