- AutorIn
- Wolfgang Lehner Technische Universität Dresden, Fakultät Informatik, Institut für Systemarchitektur, Professur Datenbanken
- Peter Benjamin VolkTechnische Universität Dresden, Fakultät Informatik, Institut für Systemarchitektur, Professur Datenbanken
- Frank RosenthalTechnische Universität Dresden, Fakultät Informatik, Institut für Systemarchitektur, Professur Datenbanken
- Martin Hahmann
- Dirk Habich
- Titel
- Clustering Uncertain Data with Possible Worlds
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-804128
- Konferenz
- IEEE 25th International Conference on Data Engineering. Shanghai, 29.03-02.04.2009
- Quellenangabe
- 2009 IEEE 25th International Conference on Data Engineering : Proceedings
Erscheinungsort: New York, NY
Verlag: Institute of Electrical and Electronics Engineers incorporated (IEEE)
Erscheinungsjahr: 2009
Seiten: 1625-1632
ISBN: 978-1-4244-3422-0 - Erstveröffentlichung
- 2009
- Abstract (EN)
- The topic of managing uncertain data has been explored in many ways. Different methodologies for data storage and query processing have been proposed. As the availability of management systems grows, the research on analytics of uncertain data is gaining in importance. Similar to the challenges faced in the field of data management, algorithms for uncertain data mining also have a high performance degradation compared to their certain algorithms. To overcome the problem of performance degradation, the MCDB approach was developed for uncertain data management based on the possible world scenario. As this methodology shows significant performance and scalability enhancement, we adopt this method for the field of mining on uncertain data. In this paper, we introduce a clustering methodology for uncertain data and illustrate current issues with this approach within the field of clustering uncertain data.
- Andere Ausgabe
- Link zum Artikel, der zuerst in der IEEE Xplore Digital Library erschienen ist:
DOI: 10.1109/ICDE.2009.174 - Freie Schlagwörter (DE)
- Gruppenzuordnung, unsichere Daten, Unsicherheit, Data-Mining, Datenbanksysteme, Clustering-Algorithmen, Datenanalyse, Degradierung, Skalierbarkeit, Datenmodelle, Datentechnik
- Freie Schlagwörter (EN)
- Clustering, Uncertain Data, Uncertainty, Data mining, Database systems, Clustering algorithms, Data analysis, Degradation, Scalability, Data models, Data engineering
- Klassifikation (DDC)
- 004
- Verlag
- IEEE, New York, NY
- Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-804128
- Veröffentlichungsdatum Qucosa
- 16.08.2022
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis