Details
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE 12th International Conference on e-Science (e-Science) |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 233-242 |
| Number of pages | 10 |
| ISBN (electronic) | 9781509042739 |
| ISBN (print) | 9781509042746, 9781509042722 |
| Publication status | Published - 6 Mar 2017 |
| Event | 12th IEEE International Conference on e-Science, e-Science 2016 - Baltimore, United States Duration: 23 Oct 2016 → 27 Oct 2016 |
Abstract
Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is choosing good features for analysis. In this paper, we introduce a data science framework that was designed to allow domain experts to consider their domain knowledge in assembling suitable data sources for complex analyses. The structure of experimental data as represented by a clustering is used to measure the relevance as well as the redundancy of each feature. We present an application of this technique to bioarchaelogical data from a region in the European Alps, a transalpine passage of eminent archaeological importance in European prehistory, the Inn-Eisack-Adige passage, spanning Italy, Austria, and Germany. These results are applied to the task of provenance analysis. The application of the presented data mining technique leads to new insights which were not found using standard bioarchaeological approaches.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Information Systems
- Environmental Science(all)
- Environmental Science (miscellaneous)
- Medicine(all)
- Medicine (miscellaneous)
- Social Sciences(all)
- Social Sciences (miscellaneous)
- Agricultural and Biological Sciences(all)
- Agricultural and Biological Sciences (miscellaneous)
- Computer Science(all)
- Computer Science Applications
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2016 IEEE 12th International Conference on e-Science (e-Science). Institute of Electrical and Electronics Engineers Inc., 2017. p. 233-242.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Applying Data Mining Methods for the Analysis of Stable Isotope Data in Bioarchaeology
AU - Mauder, Markus
AU - Ntoutsi, Eirini
AU - Kröger, Peer
AU - Mayr, Christoph
AU - Grupe, Gisela
AU - Toncala, Anita
AU - Hölzl, Stefan
N1 - Publisher Copyright: © 2016 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/3/6
Y1 - 2017/3/6
N2 - Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is choosing good features for analysis. In this paper, we introduce a data science framework that was designed to allow domain experts to consider their domain knowledge in assembling suitable data sources for complex analyses. The structure of experimental data as represented by a clustering is used to measure the relevance as well as the redundancy of each feature. We present an application of this technique to bioarchaelogical data from a region in the European Alps, a transalpine passage of eminent archaeological importance in European prehistory, the Inn-Eisack-Adige passage, spanning Italy, Austria, and Germany. These results are applied to the task of provenance analysis. The application of the presented data mining technique leads to new insights which were not found using standard bioarchaeological approaches.
AB - Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is choosing good features for analysis. In this paper, we introduce a data science framework that was designed to allow domain experts to consider their domain knowledge in assembling suitable data sources for complex analyses. The structure of experimental data as represented by a clustering is used to measure the relevance as well as the redundancy of each feature. We present an application of this technique to bioarchaelogical data from a region in the European Alps, a transalpine passage of eminent archaeological importance in European prehistory, the Inn-Eisack-Adige passage, spanning Italy, Austria, and Germany. These results are applied to the task of provenance analysis. The application of the presented data mining technique leads to new insights which were not found using standard bioarchaeological approaches.
UR - http://www.scopus.com/inward/record.url?scp=85016767489&partnerID=8YFLogxK
U2 - 10.1109/eScience.2016.7870904
DO - 10.1109/eScience.2016.7870904
M3 - Conference contribution
AN - SCOPUS:85016767489
SN - 9781509042746
SN - 9781509042722
SP - 233
EP - 242
BT - 2016 IEEE 12th International Conference on e-Science (e-Science)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on e-Science, e-Science 2016
Y2 - 23 October 2016 through 27 October 2016
ER -