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Multilevel visual representation to assist the exploration of data sets


Multidimensional projection techniques have been widely used to aid in the research and organization of data sets. In Visualization, these techniques can represent into 2D or 3D space the existing relationships from the feature spaces that describes data sets. One issue that affects the visual exploration of graphical representations generated by projection techniques is the overlapping of markers used to represent the data set instances. The overlap may result from the dimensionality reduction itself, the high similarity between the instances, and the limited screen space used to visualize the data set. In the literature, some overlap removal techniques were used to minimize the overlapping problem, however, since then the data set size has increased, also a larger screen space has been required to visualize the entire data set, requiring the user to navigate over the projected dataset. An alternative presented by some techniques is to perform a hierarchical exploration to reduce the space required to create the visual representation or to summarize the dataset. This research project has the objective of assisting the exploration of data sets through a multilevel representation of the result obtained from multidimensional projection techniques. In this way, the user will get an overview of the data set under analysis and will explore the parts of as he/she navigates the different levels of the graphical representation generated. At each level, overlapping removal techniques may be employed to avoid overlapping of markers. In addition, representative of each level will be presented to provide a summary of the groups presented to the user. (AU)

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Scientific publications (5)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MARCILIO-JR, WILSON E.; ELER, DANILO M.; GARCIA, ROGERIO E.. Contrastive analysis for scatterplot-based representations of dimensionality reduction. COMPUTERS & GRAPHICS-UK, v. 101, p. 46-58, . (18/25755-8, 18/17881-3)
MARCILIO-JR, WILSON ESTECIO; ELER, DANILO MEDEIROS. SADIRE: a context-preserving sampling technique for dimensionality reduction visualizations. JOURNAL OF VISUALIZATION, v. 23, n. 6, p. 999-1013, . (18/25755-8, 18/17881-3)
MARCILIO-JR, WILSON E.; ELER, DANILO M.; PAULOVICH, V, FERNANDO; RODRIGUES-JR, JOSE F.; ARTERO, ALMIR O.. ExplorerTree: A Focus+Context Exploration Approach for 2D Embeddings. BIG DATA RESEARCH, v. 25, . (16/11707-6, 17/17450-0, 18/17881-3, 18/25755-8)
MARCILIO-JR, WILSON E.; ELER, DANILO M.. Explaining dimensionality reduction results using Shapley values. EXPERT SYSTEMS WITH APPLICATIONS, v. 178, . (18/17881-3, 18/25755-8)
MARCILIO-JR, WILSON E.; ELER, DANILO M.; GARCIA, ROGERIO E.; CORREIA, RONALDO C. M.; RODRIGUES, RAFAEL M. B.. Visual analytics of COVID-19 dissemination in Sao Paulo state, Brazil. JOURNAL OF BIOMEDICAL INFORMATICS, v. 117, . (18/17881-3)

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