MULTIVARIATE STATISTICAL ANALYSIS OF ARCHAEOLOGICAL SAMPLES AND
THEIR VISUALISATION

a 2018

MULTIVARIATE STATISTICAL ANALYSIS OF ARCHAEOLOGICAL SAMPLES AND THEIR VISUALISATION

DILLINGEROVÁ, Veronika, Tomáš VACULOVIČ, KAterina TOMKOVA a Viktor KANICKÝ

Základní údaje

Originální název

MULTIVARIATE STATISTICAL ANALYSIS OF ARCHAEOLOGICAL SAMPLES AND THEIR VISUALISATION

Autoři

DILLINGEROVÁ, Veronika (703 Slovensko, garant, domácí), Tomáš VACULOVIČ (203 Česká republika, domácí), KAterina TOMKOVA (203 Česká republika) a Viktor KANICKÝ (203 Česká republika, domácí)

Vydání

16th Czech - Slovak Spectroscopic Conference 2018, 2018

Další údaje

Jazyk

angličtina

Typ výsledku

Konferenční abstrakt

Obor

10406 Analytical chemistry

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Kód RIV

RIV/00216224:14310/18:00106728

Organizační jednotka

Přírodovědecká fakulta

ISBN

978-80-88195-06-1

Klíčová slova anglicky

LA-ICP-MS; glass; multivariate analysis

Změněno: 28. 3. 2019 10:32, doc. Mgr. Tomáš Vaculovič, Ph.D.

Anotace

V originále

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).

Návaznosti

LQ1601, projekt VaV

Název: CEITEC 2020 (Akronym: CEITEC2020)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, CEITEC 2020

MUNI/A/1288/2017, interní kód MU

Název: Výzkum, vývoj a aplikace v analytické a fyzikální chemii (Akronym: AFCH)

Investor: Masarykova univerzita, Výzkum, vývoj a aplikace v analytické a fyzikální chemii, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

DILLINGEROVÁ, Veronika, Tomáš VACULOVIČ, KAterina TOMKOVA a 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.

@proceedings{1512317,
   author = {Dillingerová, Veronika and Vaculovič, Tomáš and Tomkova, KAterina and Kanický, Viktor},
   booktitle = {16th Czech - Slovak Spectroscopic Conference 2018},
   keywords = {LA-ICP-MS; glass; multivariate analysis},
   language = {eng},
   isbn = {978-80-88195-06-1},
   title = {MULTIVARIATE STATISTICAL ANALYSIS OF ARCHAEOLOGICAL SAMPLES AND THEIR VISUALISATION},
   year = {2018}
}

TY  - CONF
ID  - 1512317
AU  - Dillingerová, Veronika - Vaculovič, Tomáš - Tomkova, KAterina - Kanický, Viktor
PY  - 2018
TI  - MULTIVARIATE STATISTICAL ANALYSIS OF ARCHAEOLOGICAL SAMPLES AND THEIR VISUALISATION
SN  - 9788088195061
KW  - LA-ICP-MS
KW  - glass
KW  - multivariate analysis
N2  - 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).
ER  -

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

Podrobný výpis o publikaci