Abstract

Modern methods of data analysis are rarely used in archaeology. Meanwhile, it is archaeology that opens up impressive opportunities for various interdisciplinary studies at the junction of archaeology, chemistry, physics and mathematics. XRF analysis, which has long been used to determine the qualitative and quantitative composition of discovered archaeological artifacts, among other things, provides arrays of digital information that can be used by machine learning methods for more accurate clustering or classification of artifacts. This is especially true for artifacts that are presented in the form of fragments of ancient ceramic amphorae or glass vessels. Such fragments, as a rule, represent the mass of the fragments mixed among themselves. There is a need to divide them into groups and then restore them as a single artifact from the detected fragments of one group. This paper presents a comparative analysis of the application of different clustering methods to combine artifacts into groups with similar properties.

Original languageEnglish
Title of host publicationNew Knowledge in Information Systems and Technologies - Volume 1
EditorsHojjat Adeli, Luís Paulo Reis, Álvaro Rocha, Sandra Costanzo
PublisherSpringer
Pages50-57
Number of pages8
ISBN (Print)9783030161804
DOIs
Publication statusPublished - 1 Jan 2019
EventWorld Conference on Information Systems and Technologies, WorldCIST 2019 - Galicia
Duration: 16 Apr 201919 Apr 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume930
ISSN (Print)2194-5357

Conference

ConferenceWorld Conference on Information Systems and Technologies, WorldCIST 2019
CountrySpain
CityGalicia
Period16/04/1919/04/19

Fingerprint

Learning systems
Physics
Glass
Chemical analysis

Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Mikhailova, N., Mikhailova, E., & Grafeeva, N. (2019). The Application of Clustering Techniques to Group Archaeological Artifacts. In H. Adeli, L. P. Reis, Á. Rocha, & S. Costanzo (Eds.), New Knowledge in Information Systems and Technologies - Volume 1 (pp. 50-57). (Advances in Intelligent Systems and Computing; Vol. 930). Springer. https://doi.org/10.1007/978-3-030-16181-1_5
Mikhailova, N. ; Mikhailova, E. ; Grafeeva, N. / The Application of Clustering Techniques to Group Archaeological Artifacts. New Knowledge in Information Systems and Technologies - Volume 1. editor / Hojjat Adeli ; Luís Paulo Reis ; Álvaro Rocha ; Sandra Costanzo. Springer, 2019. pp. 50-57 (Advances in Intelligent Systems and Computing).
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abstract = "Modern methods of data analysis are rarely used in archaeology. Meanwhile, it is archaeology that opens up impressive opportunities for various interdisciplinary studies at the junction of archaeology, chemistry, physics and mathematics. XRF analysis, which has long been used to determine the qualitative and quantitative composition of discovered archaeological artifacts, among other things, provides arrays of digital information that can be used by machine learning methods for more accurate clustering or classification of artifacts. This is especially true for artifacts that are presented in the form of fragments of ancient ceramic amphorae or glass vessels. Such fragments, as a rule, represent the mass of the fragments mixed among themselves. There is a need to divide them into groups and then restore them as a single artifact from the detected fragments of one group. This paper presents a comparative analysis of the application of different clustering methods to combine artifacts into groups with similar properties.",
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Mikhailova, N, Mikhailova, E & Grafeeva, N 2019, The Application of Clustering Techniques to Group Archaeological Artifacts. in H Adeli, LP Reis, Á Rocha & S Costanzo (eds), New Knowledge in Information Systems and Technologies - Volume 1. Advances in Intelligent Systems and Computing, vol. 930, Springer, pp. 50-57, Galicia, 16/04/19. https://doi.org/10.1007/978-3-030-16181-1_5

The Application of Clustering Techniques to Group Archaeological Artifacts. / Mikhailova, N.; Mikhailova, E.; Grafeeva, N.

New Knowledge in Information Systems and Technologies - Volume 1. ed. / Hojjat Adeli; Luís Paulo Reis; Álvaro Rocha; Sandra Costanzo. Springer, 2019. p. 50-57 (Advances in Intelligent Systems and Computing; Vol. 930).

Research output

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Mikhailova N, Mikhailova E, Grafeeva N. The Application of Clustering Techniques to Group Archaeological Artifacts. In Adeli H, Reis LP, Rocha Á, Costanzo S, editors, New Knowledge in Information Systems and Technologies - Volume 1. Springer. 2019. p. 50-57. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-16181-1_5