<?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%">Paulo Jorge Azevedo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Rules for contrast sets</style></title><secondary-title><style face="normal" font="default" size="100%">Intelligent Data Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://haslab.uminho.pt/sites/default/files/pja/files/ida_cs.pdf</style></url></related-urls></urls><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">623-640</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this paper we present a technique to derive rules describing contrast sets. Contrast sets are a formalism to represent groups diferences. We propose a novel approach to describe directional contrasts using rules where the contrasting eect is partitioned into pairs of groups. Our approach makes use of a directional Fisher Exact Test to and signicant dierences across groups. We used a Bonferroni within search adjustment to control type I errors and a pruning technique to prevent derivation of non signicant contrast set specializations.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue></record></records></xml>