On the problem of clustering binary matrices: The spatial economic analysis case
<b>Importance</b> The article addresses binarization and clusterization of arbitrary objects' state matrices on the case of export competitiveness indicators in the ‘Fresh food’ sector of sub-Saharan African countries. <br></br> <b>Objectives</b> The purpose of the study is to develop a methodology of matrix clustering and to test it by solving the problem of spatial economic analysis. <br></br> <b>Methods</b> The article presents a methodology for matrix clusterization of objects that involves creating objects' state matrix, multi-criteria threshold binarization of state matrix and clustering the resulting binary matrix into sub-matrices with different density of zero or single elements. <br></br> <b>Results</b> The developed matrix clustering methodology was tested on export competitiveness indicators in the ‘Fresh food’ sector of sub-Saharan African countries. <br></br> <b>Conclusions</b> The developed matrix clustering methodology tested on export competitiveness indicators in the ‘Fresh food’ sector of sub-Saharan African countries shows that most of these countries are found in the second cluster (the number of zero elements in binary matrix ranges from 25 to 50%) and the third cluster (the number of zero elements in binary matrix ranges from 50 to 75%). The competitiveness of the ‘Fresh food’ sector grew from the fourth cluster to the first one. It is shown that binary matrix clustering is rather resistant to changes in threshold criteria in the initial state matrix.