Access Restriction

Author Hrr, Christian ♦ Lindinger, Elisabeth ♦ Brunnett, Guido
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Bronze Age pottery ♦ Classification theory ♦ Data mining ♦ Machine learning ♦ Similarity estimation
Abstract Formalizing and objectifying the process of artefact classification is an old wish of many archaeologists. On the other hand, data mining in general and machine learning in particular have already inspired many disciplines to introduce new paradigms of data analysis and knowledge discovery. Hence, this article aims for reviving the Typological Debate by adapting approved methods from other fields of science to archaeological data. To this end, we extensively discuss the concept of similarity and assess the suitability of machine learning techniques for the purposes of classification and typology development. Our methodology covers all steps starting from unordered, unlabelled objects to the emergence of a consistent and reusable typology. The application of this process is exemplarily illustrated by classifying the vessels from a Late Bronze Age cemetery in Eastern Saxony. Despite the individual character of these vessels, we achieved class prediction rates of more than 95%. Such a success was only possible, because we permanently reconciled the output of the learning algorithms with our own expectations in order to identify and eliminate the systematic errors within the typology.
ISSN 15564673
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-04-01
Publisher Place New York
e-ISSN 15564711
Journal Journal on Computing and Cultural Heritage (JOCCH)
Volume Number 7
Issue Number 1
Page Count 23
Starting Page 1
Ending Page 23

Open content in new tab

   Open content in new tab
Source: ACM Digital Library