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Matrix reordering algorithms provide an initial permutation of these matrices, which should help to reveal hidden patterns in the dataset in the visual structure. Some of these algorithms directly permute the data matrix, instead of its row- and column-proximity matrices. We present a data matrix reordering method ( feature vector-based sort \u2013 FVS), which reorders a data matrix aiming to reveal simplex and equi-correlation patterns. Our approach extracts feature vectors from a data matrix and uses them to calculate row and column permutations of the data matrix. We used FVS for reordering data matrices of distinct real-world scenarios, in which it revealed those patterns. Our experiments with synthetic matrices revealed that FVS is faster than other known matrix-reordering algorithms and produces results of approximately the same quality (in terms of stress function) when these patterns are hidden in the data matrix. We also present some real-world datasets reordered by our algorithm and discuss the patterns that it uncovers.<\/jats:p>","DOI":"10.1177\/1473871616666392","type":"journal-article","created":{"date-parts":[[2016,9,8]],"date-time":"2016-09-08T21:44:12Z","timestamp":1473371052000},"page":"261-274","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["A fast feature vector approach for revealing simplex and equi-correlation data patterns in reorderable matrices"],"prefix":"10.1177","volume":"16","author":[{"given":"Celmar","family":"Guimar\u00e3es da Silva","sequence":"first","affiliation":[{"name":"Software Engineering and Information Systems Laboratory (SEIS), School of Technology, University of Campinas (Unicamp), S\u00e3o Paulo, Brazil"}]},{"given":"Bruno Figueiredo","family":"Medina","sequence":"additional","affiliation":[{"name":"Software Engineering and Information Systems Laboratory (SEIS), School of 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