@conference {DBLP:conf/pakdd/SaSJAC11,
	title = {Mining Association Rules for Label Ranking},
	booktitle = {PAKDD - The 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining},
	year = {2011},
	month = {May},
	pages = {432-443},
	address = {Shenzhen, China},
	abstract = {<p>Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we continue this line of work by proposing an adaptation of association rules for label ranking based on the APRIORI algorithm. Given that the original APRIORI algorithm does not aim to obtain predictive models, two changes were needed for this achievement. The adaptation essentially consists of using variations of the support and condence measures based on ranking similarity functions that are suitable for label ranking. Additionally we propose a simple greedy method to select the parameters of the algorithm. We also adapt the method to make a prediction from the possibly con icting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classication, partial results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.</p>
},
	attachments = {https://haslab.uminho.pt/sites/default/files/pja/files/13-sa.pdf},
	author = {Cl{\'a}udio Rebelo S{\'a} and Carlos Soares and Al{\'\i}pio M{\'a}rio Jorge and Paulo Jorge Azevedo and Joaquim Pinto Costa}
}