<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Orestes Cerdeira</style></author><author><style face="normal" font="default" size="100%">Tiago Monteiro-Henriques</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The R Journal: Clustering Binary Data Optimizing the Exclusiveness of Features</style></title><secondary-title><style face="normal" font="default" size="100%">The R Journal</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://journal.r-project.org/articles/RJ-2026-020/</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">329-343</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt; A problem that has been studied for a long time in Vegetation Science consists in finding distinct groups among a collection of vegetation samples (relevés). Recently a criterion has been proposed to capture the patterns of differential species among the groups from any arbitrary M-cluster of relevés. The criterion optimization is quite complex (NP-hard) as it implies searching a huge set of possible combinations of relevés into M groups. In this paper we give an integer linear programming formulation to optimize the criterion for M=2, that can be used to solve moderately-sized data problems; we also describe a greedy randomized adaptive search procedure and a simulated annealing algorithm for arbitrary M. We combined these different approaches and prepared a collection of software functions, which are available in the open-source R package diffval. We illustrate the use of such functions using real-world data.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;&lt;a href=&quot;https://doi.org/10.32614/RJ-2026-020&quot;&gt;https://doi.org/10.32614/RJ-2026-020&lt;/a&gt;&lt;/p&gt;
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