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.
|Title of host publication||Large Scale Data Analytics|
|Editors||Aris Gkoulalas-Divanis, Abderrahim Labbi|
|Place of Publication||New York, NY|
|Number of pages||30|
|Publication status||Published - 2014|