2015
DOI: 10.1016/j.clay.2014.12.020
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African terra sigillata from Henchir Es-Srira archaeological site, central Tunisia: Archaeological provenance and raw materials based on chemical analysis

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Cited by 9 publications
(1 citation statement)
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“…Unlike chemical data, on which multivariate statistical tools have long been applied in studying ancient pottery (methodological papers: Mommsen et al, 1988;Tangri and Wright, 1993;Baxter, 1994Baxter, , 1999Baxter, , 2006Baxter, , 2008Neff, 1994;Buxeda i Garrigós J., 1999;Baxter and Buck, 2000; some case studies: Maritan, 2004;Maritan et al, 2013;Holakooei et al, 2013;Baklouti et al, 2015), cluster analysis has only recently been applied to XRPD data in archaeometry, to study the production recipes of ancient mortars (Piovesan et al, 2013) and in the geological field, for grade control of iron ores (Paine et al, 2012); principal component analysis has been performed on Iranian tiles (Holakooei et al, 2014). The great potential of cluster analysis of XRPD data is that it can compare the similarity of the whole dataset, through the "figure of merit", which quantifies matching among scans and does not require a priori quantification of the phases composing each sample.…”
Section: Introductionmentioning
confidence: 98%
“…Unlike chemical data, on which multivariate statistical tools have long been applied in studying ancient pottery (methodological papers: Mommsen et al, 1988;Tangri and Wright, 1993;Baxter, 1994Baxter, , 1999Baxter, , 2006Baxter, , 2008Neff, 1994;Buxeda i Garrigós J., 1999;Baxter and Buck, 2000; some case studies: Maritan, 2004;Maritan et al, 2013;Holakooei et al, 2013;Baklouti et al, 2015), cluster analysis has only recently been applied to XRPD data in archaeometry, to study the production recipes of ancient mortars (Piovesan et al, 2013) and in the geological field, for grade control of iron ores (Paine et al, 2012); principal component analysis has been performed on Iranian tiles (Holakooei et al, 2014). The great potential of cluster analysis of XRPD data is that it can compare the similarity of the whole dataset, through the "figure of merit", which quantifies matching among scans and does not require a priori quantification of the phases composing each sample.…”
Section: Introductionmentioning
confidence: 98%