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Auflistung nach Autor:in "Plant, Claudia"

1 - 6 von 6
  • Textdokument
    Data Mining Algorithmen zur Wissensgewinnung aus komplexen biologischen und medizinischen Datensätzen
    (Ausgezeichnete Informatikdissertationen 2007, 2008) Plant, Claudia
  • Konferenzbeitrag
    DipTransformation: Enhancing the Structure of a Dataset and thereby improving Clustering
    (INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, 2019) Schelling, Benjamin; Plant, Claudia
  • Konferenzbeitrag
    Discovering Non-Redundant K-means Clusterings in Optimal Subspaces
    (INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, 2019) Mautz, Dominik; Ye, Wei; Plant, Claudia; Böhm, Christian
  • Konferenzbeitrag
    Index- supported similarity join on graphics processors
    (Datenbanksysteme in Business, Technologie und Web (BTW) – 13. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2009) Böhm, Christian; Noll, Robert; Plant, Claudia; Zherdin, Andrew
    The similarity join is an important building block for similarity search and data mining algorithms. In this paper, we propose an algorithm for similarity join on Graphics Processing Units (GPUs). As major advantages GPUs provide extremely high parallelis
  • Konferenzbeitrag
    Model-based classification of data with time series-valued attributes
    (Datenbanksysteme in Business, Technologie und Web (BTW) – 13. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2009) Böhm, Christian; Läer, Leonhard; Plant, Claudia; Zherdin, Andrew
    Similarity search and data mining on time series databases has recently attracted much attention. In this paper, we represent a data object by several time series-valued attributes. Although this kind of object representation is very natural and straightf
  • Zeitschriftenartikel
    The Data Mining Group at University of Vienna
    (Datenbank-Spektrum: Vol. 20, No. 1, 2020) Altinigneli, Can; Bauer, Lena Greta Marie; Behzadi, Sahar; Fritze, Robert; Hlaváčková-Schindler, Kateřina; Leodolter, Maximilian; Miklautz, Lukas; Perdacher, Martin; Sadikaj, Ylli; Schelling, Benjamin; Plant, Claudia
    How can we extract meaningful knowledge from massive amounts of data? The data mining group at University of Vienna contributes novel methods for exploratory data analysis. Our main research focus is on unsupervised learning, where we want to identify any kind of non-random structure or patterns in the data without restricting ourselves to a pre-defined target variable or analysis goal. Our major lines of current research are clustering, causality detection and highly efficient exploratory data analysis on massive data. Besides that, we develop application-specific methods addressing specific challenges in biomedicine, neuroscience and environmental sciences. In teaching, we offer fundamental and advanced courses in data mining, machine learning and scientific data management for Bachelor and Master students of computer science and related programs.