Spatial k-means clustering in archaeology -- variations on a theme

Journal of Archaeological Science : Reports, 2017

A B S T R A C T 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.

Archaeometry, 2008

An approach to testing for modes in low-dimensional data, Silverman's test, novel in an archaeological setting, is described and illustrated. 'Patterns' in archaeological data can be suggested by the presence of modes. Reassurance is needed that modes suggested by graphical analysis are genuine before attempting substantive archaeological interpretation. The test either provides such reassurance, or else guards against over-interpretation, particularly with small samples. Data on loomweight dimensions, lead isotope ratios, and ceramic compositions are used to illustrate use of the test, dealing with issues concerning outliers and small samples as they arise. The focus is on univariate mode detection.

K-means spatial clustering, or pure locational clustering, has been a popular tool for spatial data analysis in archaeology since its introduction by Kintigh and Ammerman in 1982. Among its acknowledged limitations is the problem of choosing an appropriate level of clustering and, more seriously, the fact that the method tends to produce circular clusters of equal size, regardless of the true underlying structure. This paper draws on recent developments in the statistical literature to show how these problems can be overcome, and illustrates the methodology on both simulated and real data. What emerges is presented not as an alternative to k-means clustering as usually practiced, but as a method in the same spirit that overcomes some of its limitations.

lawas.co.nz

Information theory provides a powerful method of analysing and clustering archaeological data that exists in categorised form. The approach can be used to cluster entities hierarchically and select optimal levels of clustering based internally on relative information or on having the maximum interaction with archaeological provenance.

2008

2014

order to understand the funerary rituals and the formation of cemeteries. The point pattern data from the 10th–13th century AD cemetery at Madi in Estonia were analysed using several intrasite spatial and geostatistical methods such as autocorrelation, nearest neighbour analysis, point density analysis, and minimum distance analysis. As a result, a new understanding of the funerary rituals performed on the burial place and formation process of the dispersed cremation cemetery is presented.

Computer Applications in Archaeology proceedings (CAA99), published 2004, 2004

This is the record of a conference presentation at the Computer Applications in Archaeology conference of 1999, not published, for various reasons, until 2004. The main interest lies, if anywhere, in the application of the clustering of kernel density estimates applied to art historical data derived via scientific methodology.

Antiquity, 2020

Lock. 2020. Archaeological spatial analysis: a methodological guide. London: Routledge; 978-0-8153-7323-6 hardback £96.

Space - Archaeology’s Final Frontier? An Intercontinental Approach, 2007

In the past it was said that the quality of archaeological spatial data had moved beyond the methods available to analyze it (Ammermen 1992). This is no longer true given the recent rapid development of technologies such as Geographic Information Systems (GIS) that accurately handle large quantities and types of data. What is lacking is a coherent body of theory to guide interpretation in light of the analyses possible due to advancing technologies. In interpretation, spatial designations are of no small consequence, and an agreed upon, pan-archaeological definition and conceptual framework for space is unlikely. As Agnew (1993:251) argued over a decade ago, "representations of geographical space have not elicited much attention" from non-geographers. However, a consistent dialogue about the ontology and epistemology of space in archaeology is both possible and necessary, and this volume represents a decided move in that direction. Because space is one of the essential elements of any archaeological study, finding a common ground to address spatial phenomena is vital. There is no question that the authors in this volume deliver different approaches to spatial problems, but common threads can be spun from their work.