This chapter describes ideas that have been put forward for understanding the contents of text collections from a more analytical point of view.
Visualization for text mining:
1.TAKMI Text Mining System
2.Jigsaw system (Gorg et al., 2007) was designed to allow intelligence analysts to examine relationships among entities mentioned in a document collection and phone logs.
3.The BETA system, part of the IBM Web Fountain project (Meredith and Pieper, 2006), also had the goal of facilitating exploration of data within dimensions automatically extracted from text.
4. The TRIST information “triage” system (Jonker et al., 2005, Proulx et al., 2006) attempted to address many of the deficiencies of standard search for information analysts' tasks.
1.2 The work Frequency of the visualization
1.The SeeSoft visualization (Eick, 1994) represented text in a manner resembling columns of newspaper text, with one “line” of text on each horizontal line of the strip
2. The TextArc visualization (Paley, 2002) is similar to SeeSoft, but arranged the lines of text in a spiral and placed frequently occurring words within the center of the spiral.
1.3 Visualization And relationship
Some more recent approaches have moved away from nodes-and-links in order to break the interesting relationships into pieces and show their connections via brushing-and-linking interactions.