MULTIVARIATE STATISTICAL ANALYSIS OF ARCHAEOLOGICAL SAMPLES AND THEIR VISUALISATION

DILLINGEROVÁ, Veronika, Tomáš VACULOVIČ, KAterina TOMKOVA and Viktor KANICKÝ. MULTIVARIATE STATISTICAL ANALYSIS OF ARCHAEOLOGICAL SAMPLES AND THEIR VISUALISATION. In 16th Czech - Slovak Spectroscopic Conference 2018. 2018. ISBN 978-80-88195-06-1.

Basic information

• Original name: MULTIVARIATE STATISTICAL ANALYSIS OF ARCHAEOLOGICAL SAMPLES AND THEIR VISUALISATION
• Authors: DILLINGEROVÁ, Veronika (703 Slovakia, guarantor, belonging to the institution), Tomáš VACULOVIČ (203 Czech Republic, belonging to the institution), KAterina TOMKOVA (203 Czech Republic) and Viktor KANICKÝ (203 Czech Republic, belonging to the institution).
• Edition: 16th Czech - Slovak Spectroscopic Conference 2018, 2018.

Other information

• Original language: English
• Type of outcome: Conference abstract
• Field of Study: 10406 Analytical chemistry
• Country of publisher: Czech Republic
• Confidentiality degree: is not subject to a state or trade secret
• RIV identification code: RIV/00216224:14310/18:00106728
• Organization unit: Faculty of Science
• ISBN: 978-80-88195-06-1
• Keywords in English: LA-ICP-MS; glass; multivariate analysis

Abstract

Historical contact between Bavaria and Bohemia was shown in different fields. Archaeology was also influenced by that, so when it comes to findings from these two areas, it is important to investigate their connection. This work aims to find differences or similarities between glass beads found in Bavaria and Bohemia. Elemental composition of glass beads was measured by LA-ICP-MS. From an archaeological point of view, it is usually important to compare main oxides, such as K2O and MgO to differentiate soda-lime natron glass from plant ash glass. However, this study focusses on the use of machine learning algorithms to find patterns in trace elements of the glass beads. With a low number of observations - glass beads (only 51) it is important to find the most suitable algorithm. Therefore, we decided to compare multiple machine learning algorithms for this data set. Its main purpose is to show the possibilities of complex algorithms, such as k-nearest neighbours (KNN), decision trees, e.g. random forest (RF) or linear discriminant analysis (LDA). First of all, an exploratory analysis was performed to determine variables - elements with high distinguishing power. Programming language python with sci-kit learn library and software SAS Enterprise guide was used. This study compared multiple machine learning algorithms for finding differences and similarities of the set of glass beads from Bohemia and Bavaria. These algorithms work on different principles, so in some cases it can be beneficial to use more than just a one. Especially in this one, when each one of them can discover various interesting facts about the observed dataset. Acknowledgment This work was supported by the project CEITEC2020 (LQ1601) made by Ministry of Education, Youth and Sports of the Czech Republic and Student Project Grant at MU (specific research, rector's programme) - Category A (MUNI/A/1288/2017).

Links

• LQ1601, research and development project: Name: CEITEC 2020 (Acronym: CEITEC2020)

Investor: Ministry of Education, Youth and Sports of the CR

• MUNI/A/1288/2017, interní kód MU: Name: Výzkum, vývoj a aplikace v analytické a fyzikální chemii (Acronym: AFCH)

Investor: Masaryk University, Category A