Hybrid visualization approach to show documents similarity and content in a single view

• Autor(a) principal: Andreotti, Andre Luiz Dias [UNESP]
• Data de Publicação: 2018
• Outros Autores: Silva, Lenon Fachiano [UNESP], Eler, Danilo Medeiros [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.3390/info9060129; http://hdl.handle.net/11449/179939
• topic: Document pre-processing; Document similarity; Hybrid visualization; Multidimensional projection; Tag cloud; Text mining

Multidimensional projection techniques can be employed to project datasets from a higher to a lower dimensional space (e.g., 2D space). These techniques can be used to present the relationships of dataset instances based on distance by grouping or separating clusters of instances in the projected space. Several works have used multidimensional projections to aid in the exploration of document collections. Even though the projection techniques can organize a dataset, the user needs to read each document to understand the cluster generation. Alternatively, techniques such as topic extraction or tag clouds can be employed to present a summary of the document contents. To minimize the exploratory work and to aid in cluster analysis, this work proposes a new hybrid visualization to show both document relationship and content in a single view, employing multidimensional projections to relate documents and tag clouds. We show the effectiveness of the proposed approach in the exploration of two document collections composed by world news.