Models of incremental concept formation
Artificial Intelligence
A vector space model for automatic indexing
Communications of the ACM
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
Evidence-Based Software Engineering for Practitioners
IEEE Software
Visual text mining using association rules
Computers and Graphics
A Visual Text Mining approach for Systematic Reviews
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
The Projection Explorer: A Flexible Tool for Projection-based Multidimensional Visualization
SIBGRAPI '07 Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing
IEEE Transactions on Visualization and Computer Graphics
Topic-Based Coordination for Visual Analysis of Evolving Document Collections
IV '09 Proceedings of the 2009 13th International Conference Information Visualisation
The automatic creation of literature abstracts
IBM Journal of Research and Development
ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
Systematic mapping studies in software engineering
EASE'08 Proceedings of the 12th international conference on Evaluation and Assessment in Software Engineering
From visual data exploration to visual data mining: a survey
IEEE Transactions on Visualization and Computer Graphics
A visual analysis approach to validate the selection review of primary studies in systematic reviews
Information and Software Technology
SLuRp: a tool to help large complex systematic literature reviews deliver valid and rigorous results
Proceedings of the 2nd international workshop on Evidential assessment of software technologies
RAMANI: Uma Ferramenta de Apoio à Colaboração durante a Execução de Estudos Sistemáticos
Proceedings of the X Brazilian Symposium in Collaborative Systems
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Context: Systematic mapping provides an overview of a research area to assess the quantity of evidence existing on a topic of interest. In spite of its relevance, the establishment of consistent categories and classification of primary studies in these categories are manually conducted. Objective: We propose an approach, named SM-VTM (Systematic Mapping based on Visual Text Mining), to support categorization and classification stages in the systematic mapping using Visual Text Mining (VTM), aiming at reducing time and effort required in this process. Method: We established SM-VTM, selected a VTM tool and conducted a case study comparing results of two systematic mappings: one performed manually and another using our approach. Results: The results of both systematic mappings were very similar, showing the viability of SM-VTM. Furthermore, since our approach was applied using a tool, reduction of time and effort can be achieved. Conclusions: The application of VTM seems to be very relevant in the context of systematic mapping.