期刊
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
卷 14, 期 6, 页码 1229-1236出版社
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2008.138
关键词
Text and document visualization; hierarchical multidimensional visualization; visual knowledge discovery; high-dimensional data
资金
- FAPESP research financial agency, Sao Paulo, Brazil [04/07866-4, 04/09888-5]
- CAPES research financial agency, Brazil [2214-07-5]
- CNPq research financial, Brazil [304758/2005-1, 484256/2007-6]
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [04/09888-5] Funding Source: FAPESP
Point placement strategies aim at mapping data points represented in higher dimensions to bi-dimensional spaces and are frequently used to visualize relationships amongst data instances. They have been valuable tools for analysis and exploration of data sets of various kinds. Many conventional techniques, however, do not behave well when the number of dimensions is high, such as in the case of documents collections. Later approaches handle that shortcoming, but may cause too much clutter to allow flexible exploration to take place. In this work we present a novel hierarchical point placement technique that is capable of dealing with these problems. While good grouping and separation of data with high similarity is maintained without increasing computation cost, its hierarchical structure lends itself both to exploration in various levels of detail and to handling data in subsets, improving analysis capability and also allowing manipulation of larger data sets.
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