Skip to main content
Log in

Ceramic investigation: how to perform statistical analyses

  • Original Paper
  • Published:
Archaeological and Anthropological Sciences Aims and scope Submit manuscript

Abstract

The aim of this article is to summarize and organize the methodologies used for the statistical analysis in the field of ceramic investigation and, more specifically, the study of ceramic provenance. An update and review of all related methodologies is provided during the presentation of a typical statistical analysis. The presentation is given in a step-by-step process and emphasis is on interpretation of the intermediate and final results. The analysis attempts to cover the following:

  • What issues to examine in a preliminary analysis

  • Data transformation

  • Cluster analysis

  • Clustering assessment

  • Data dimension reduction methods as part of a clustering visualization and assessment

  • Outliers and small groups

  • Mixed-mode analysis

  • Cluster characterization and discriminating factors

  • Classification

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Aitchison J (1986) The statistical analysis of compositional data. Chapman & Hall, London

    Book  Google Scholar 

  • Aloupi-Siotis E (2020) Ceramic technology. How to characterise black Fe-based glass-ceramic coatings. Archaeol Anthrop Sci. https://doi.org/10.1007/s12520-020-01134-x

  • Angourakis A, MartínezFerreras V, Torrano A, GurtEsparraguera JM (2018) Presenting multivariate statistical protocols in R using Roman wine amphorae productions in Catalonia, Spain. J Archaeol Sci 93:150–165

    Article  Google Scholar 

  • Banfield JD, Raftery AE (1993) Model-based Gaussian and non-Gaussian clustering. Biometrics 49:803–821

    Article  Google Scholar 

  • Baker FB, Hubert LJ (1975) Measuring the power of hierarchical cluster analysis. J Am Stat Assoc 70:31–38

    Article  Google Scholar 

  • Barcelo JA, Bogdanovic I (2015) Mathematics and archaeology. CRC Press, Boca Raton

    Book  Google Scholar 

  • Baxter MJ (1995) Standardization and transformation in principal component analysis, with applications to archaeometry. Applied Statist 44(4):513–527

    Article  Google Scholar 

  • Baxter MJ (2001) Statistical modelling of artefact compositional data. Archaeometry 43(1):131–147

    Article  Google Scholar 

  • Baxter MJ (2006) A review of supervised and unsupervised pattern recognition in archaeometry. Archaeometry 48(4):671–694

    Article  Google Scholar 

  • Baxter MJ (2008) Mathematics, statistics and Archaeometry – the last 50 years or so. Archaeometry 50(6):968–982

    Article  Google Scholar 

  • Baxter MJ (2015a) Exploratory multivariate analysis in archaeology (foundations of archaeology), 2nd edn. Eliot Werner Publications/Percheron Press

  • Baxter MJ (2015b) Spatial k-means clustering in archaeology – variations on a theme. Working paper – November 2015 (accessed in Academia.edu)

  • Baxter MJ, Beardah CC, Papageorgiou I, Cau PM, Day PM, Kilikoglou V (2008) On statistical approaches to the study of ceramic Artefacts using geochemical and petrographic data. Archaeometry 50:142–157. https://doi.org/10.1111/J.1475-4754.2007.00359.X

    Article  Google Scholar 

  • Beardah CC, Baxter MJ, Cool HEM, Jackson CM (2003) Compositional data analysis of archaeological glass: problems and possible solutions. CoDaWork’03: Compositional Data Analysis Workshop, Girona, Spain Available at http://ima.udg.es/Activitats/CoDaWork03/paper\_baxter\_Beardah2.pdf

    Google Scholar 

  • Bieber AM Jr, Brooks DW, Harbottle G, Sayre EV (1976) Application of multivariate techniques to analytical data on Aegean ceramics. Archaeometry 18:59–74

    Article  Google Scholar 

  • Binford LR (1964) A consideration of archaeological research design. Am Antiq 29(4):425–441

    Article  Google Scholar 

  • Campello RJGB (2007) A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment. Pattern Recogn Lett 28:833–841

    Article  Google Scholar 

  • Charrad M, Ghazzali N, Boiteau V, Niknafs A (2014) NbClust: an R package for determining the relevant number of clusters in a data set. J Stat Softw 61(6):1–36 http://www.jstatsoft.org/v61/i06/

    Article  Google Scholar 

  • de Lapérouse J-F (2020) Ceramic musealisation: How ceramics are conserved and the implications for research. Archaeol Anthrop Sci. https://doi.org/10.1007/s12520-020-01139-6

  • Drennan RD (2009) Statistics for archaeologists. In: A common sense approach, Second edn. Springer

  • Egozcue JJ (2009) Reply to “On the Harker Variation Diagrams; …” by J.A. Cortés. Math Geosci 41:829–834

    Article  Google Scholar 

  • Eramo G (2020) Ceramic technology. How to recognize clay processing. Archaeol Anthrop Sci. https://doi.org/10.1007/s12520-020-01132-z

  • Everitt BS, Landau S, Leese M (2011) Cluster analysis, 5th edn. John Wiley & Sons

  • Everitt BS, Dunn G (2001) Applied multivariate data analysis, 2nd edn. John Wiley & Sons

  • Fowlkes EB, Mallows CL (1983) A method for comparing two hierarchical clusterings. J Am Stat Assoc 78:553–569

    Article  Google Scholar 

  • Filzmoser P, Garrett RG, Reimann R (2005) Multivariate outlier detection in exploration geochemistry. Comput Geosci 31:579–587

    Article  Google Scholar 

  • Filzmoser P, Hron K, Templ M (2018) Applied compositional data analysis with worked examples in R. Springer

  • Galli A, Sibilia E, Martini M (2020) Ceramic chronology by luminescence dating. How and when it is possible to date ceramic artefacts. Archaeol Anthrop Sci. https://doi.org/10.1007/s12520-020-01140-z

  • Gliozzo E (2020a) Ceramics investigation, Research questions and sampling criteria. Archaeol Anthrop Sci. https://doi.org/10.1007/s12520-020-01128-9

  • Gliozzo E (2020b) Ceramic technology. How to reconstruct the firing process. Archaeol Anthrop Sci. https://doi.org/10.1007/s12520-020-01133-y

  • Glascock MD (2016) Compositional analysis in archaeology. Oxford Handbooks, Oxford Handbooks Online, pp 1–25

    Book  Google Scholar 

  • Greenacre M (2018) Compositional data analysis in practice. Chapman and Hall/CRC

  • Gower JC (1971) A general coefficient of similarity and some of its properties. Biometrics 27(4):857–871

    Article  Google Scholar 

  • Gower JC, Legendre P (1986) Metric and Euclidean properties of dissimilarity coefficients. J Classif 5:5–48

    Article  Google Scholar 

  • Gualtieri S (2020) Ceramic raw materials, How to establish the technological suitability of a raw material. Archaeol Anthrop Sci. https://doi.org/10.1007/s12520-020-01135-w

  • Halkidi M, Batistakis Y, Vazirgiannis M (2001) On clustering validation techniques. J Intell Inf Syst 17:107–145

    Article  Google Scholar 

  • Hein A, Kilikoglou V (2020) Ceramic raw materials, How to recognize them and locate the supply basins. Chemistry. Archaeol Anthrop Sci. https://doi.org/10.1007/s12520-020-01129-8

  • Henderson J, Ma H, Cui J, Ma R, Xiao H (2020) Isotopic investigations of Chinese ceramics. Archaeol Anthrop Sci. https://doi.org/10.1007/s12520-020-01138-7

  • Hubert L, Arabie P (1985) Comparing partitions. J Classif 2:193–218

    Article  Google Scholar 

  • Ionescu C, Hoeck V (2020) Ceramic technology. How to investigate surface finishing. Archaeol Anthrop Sci. https://doi.org/10.1007/s12520-020-01144-9

  • James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning with applications in R. Springer, New York

    Book  Google Scholar 

  • Jolliffe IT (2002) Principal component analysis. Springer

  • Kassambara A (2017) Practical guide to cluster analysis. In: R. Unsupervised machine learning: volume 1 (multivariate analysis). STHDA Publishing

  • Maritan L (2020) Ceramic abandonment. How to recognise post-depositional transformations. Archaeol Anthrop Sci. https://doi.org/10.1007/s12520-020-01141-y

  • Martín-Fernández JA, Buxeda i Garrigós J, Pawlowsky-Glahn V (2015) Logratio analysis in archeometry: principles and methods. In: Barcelo JA, Bogdanovic I (eds) Mathematics and archaeology. CPC press, Boca Raton FL, pp 178–189

  • Montana G (2020) Ceramic raw materials. How to recognize them and locate the supply basins. Mineralogy, Petrography. Archaeol Anthrop Sci. https://doi.org/10.1007/s12520-020-01130-1

  • Palarea-Albaladejo J, Martín-Fernández JA (2015) zCompositions — R package for multivariate imputation of left-censored data under a compositional approach. Chemom Intell Lab Syst 143:85–96

    Article  Google Scholar 

  • Papageorgiou I (2018) Cluster analysis. In: The SAS Encyclopedia of Archaeological Sciences. John Wiley & Sons. https://doi.org/10.1002/9781119188230.saseas0099

  • Papageorgiou I, Baxter MJ, Cau MA (2001) Model-based cluster analysis of artefact compositional data. Archaeometry 43:571–588

    Article  Google Scholar 

  • Papageorgiou I, Moustaki I (2005) Latent class models for mixed variables with applications in archaeometry. Comput Stat Data An 48:659–675

    Article  Google Scholar 

  • Pawlowsky-Glahn V (2003) Statistical modelling on coordinates. In: Thio-Henestrosa S, Martin-Fernandez JA (eds) Compositional data analysis workshop-CoDaWork’03, Proceedings. University of Girona, Girona

    Google Scholar 

  • Pawlowsky-Glahn V, Egozcue JJ, Tolosana-Delgado R (2015) Modeling and analysis of compositional data. John Wiley & Sons, Springer, London, UK

    Google Scholar 

  • Pradell T, Molera J (2020) Ceramic technology. How to characterise ceramic glazes. Archaeol Anthrop Sci. https://doi.org/10.1007/s12520-020-01136-9

  • Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc:846–850

  • Rogers S, Girolami M (2016) A first course in machine learning, 2nd edn. Chapman and Hall

  • Rousseeuw P, Van Zomeren B (1990) Unmasking multivariate outliers and leverage points. J Am Stat Assoc 85:633–639

    Article  Google Scholar 

  • Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  Google Scholar 

  • Saraçli S, Doğan N, Doğan İ (2013) Comparison of hierarchical cluster analysis methods by cophenetic correlation. J InequalAppl 2013:203. https://doi.org/10.1186/1029-242X-2013-203

    Article  Google Scholar 

  • Sciau P, Sanchez C, Gliozzo E (2020) Ceramic technology. How to characteriseterra sigillata ware. J Am Stat Assoc. https://doi.org/10.1007/s12520-020-01137-8

  • Shotwell MS (2013) profdpm: an R package for MAP estimation in a class of conjugate product partition models. J Stat Soft 53:1-18. http://www.jstatsoft.org/v53/i08/

  • Sokal RR, Rohlf FJ (1962) The comparison of dendrograms by objective methods. Taxon 11:33–40

    Article  Google Scholar 

  • Steinley D (2006) K-means clustering: a half-century synthesis. Br J Math Stat Psychol 59:1–34

    Article  Google Scholar 

  • Schwarz G (1978) Estimating the dimension of the model. Ann Stat 6:461–464

    Article  Google Scholar 

  • Thér R (2020) Ceramic technology, How to reconstruct and describe pottery-forming practices. Archaeol Anthrop Sci. https://doi.org/10.1007/s12520-020-01131-0

  • Thomas DH (1978) The awful truth about statistics in archaeology. Am Antiq 43:231–244

    Article  Google Scholar 

  • Van den Boogaart KG, Tolosana-Delgado R (2013) Analyzing compositional data with R. Springer, Berlin Heidelberg

    Book  Google Scholar 

  • Whallon R (1984) Unconstrained clustering for the analysis of spatial distributions in archaeology. In: Hietala HJ (ed) Intrasite spatial analysis in archaeology. Cambridge University Press, New York, pp 242–277

    Google Scholar 

  • Wickham H (2009) ggplot2: elegant graphics for data analysis. Springer, New York

  • Xu R, Wunsch-II DC (2008) Clustering. Wiley, John & Sons, Inc

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ioulia Papageorgiou.

Ethics declarations

Conflict of interest

The author declares that there are no conflicts of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is a Topical Collection on Ceramics: Research questions and answers

Electronic supplementary material

ESM 1

(DOCX 19 kb)

ESM 2

(DOCX 20 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Papageorgiou, I. Ceramic investigation: how to perform statistical analyses. Archaeol Anthropol Sci 12, 210 (2020). https://doi.org/10.1007/s12520-020-01142-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12520-020-01142-x

Keywords

Navigation