Quality analysis of matrix reordering algorithms related to information visualization

• Grant number: 14/11186-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: September 01, 2014
• End date: July 31, 2015
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Celmar Guimarães da Silva
• Grantee: Willian Hitoshi Kawakami
• Host Institution: Faculdade de Tecnologia (FT). Universidade Estadual de Campinas (UNICAMP). Limeira , SP, Brazil

Abstract

Matrices are structures, which underlie a variety of data visualization techniques, such as heatmaps. Distinct algorithms enable to permute its elements aiming to provide a better visual understanding, by grouping similar rows or columns, or by highlighting patterns. Previous works used Matrix Reordering Analyzer (MRA) tool for creating new reordering algorithms (PQR Sort and PQR Sort SR), and also for comparing reordering quality and execution time of a set of algorithms. However, the set of algorithms implemented into MRA is still small, which hampers to reach a result that could present the state-of-the-art. Therefore, this research project aims to compare state-of-the-art matrix reordering algorithms, in order to validate the already obtained results of PQR Sort and PQR Sort SR algorithms.

Scientific publications

(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)

DA SILVA, CELMAR GUIMARAES; MEDINA, BRUNO FIGUEIREDO; DA SILVA, MARESSA RODRIGUES; KAWAKAMI, WILLIAN HITOSHI; NAVES ROCHA, MIGUEL MECHI. A fast feature vector approach for revealing simplex and equi-correlation data patterns in reorderable matrices. INFORMATION VISUALIZATION, v. 16, n. 4, p. 261-274, OCT 2017. (15/14854-7, 14/11186-0, 15/00411-6)