Visual Analysis and Knowledge Discovery for Text

Christin Seifert, Vedran Sabol, Wolfgang Kienreich, Elisabeth Lex, Michael Granitzer

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    8 Citations (Scopus)

    Abstract

    Providing means for effectively accessing and exploring large textual data sets is a problem attracting attention of text mining and information visualization experts alike. Rapid growth of the data volume, heterogeneity and richness of metadata, and the dynamic nature of text repositories add to the complexity of the task. This chapter provides an overview of visualization methods for gaining insight into large, heterogeneous, dynamic textual data sets. We argue that visual analysis in combination with automatic knowledge discovery methods provides several advantages. Besides introducing human knowledge and visual pattern recognition into the analytical process, it provides the possibility to improve the performance of automatic methods through user feedback.
    Original languageEnglish
    Title of host publicationLarge Scale Data Analytics
    EditorsAris Gkoulalas-Divanis, Abderrahim Labbi
    Place of PublicationNew York, NY
    PublisherSpringer
    Pages189-218
    Number of pages30
    ISBN (Electronic)978-1-4614-9242-9
    ISBN (Print)978-1-4614-9241-2
    DOIs
    Publication statusPublished - 2014

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