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<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/"><rdf:Description rdf:about="https://dk.um.si/IzpisGradiva.php?id=65060"><dc:title>Algorithms for association rule learning</dc:title><dc:creator>Akhmetshakirova,	Renata	(Avtor)
	</dc:creator><dc:creator>Strnad,	Damjan	(Mentor)
	</dc:creator><dc:subject>association rules</dc:subject><dc:subject>data mining</dc:subject><dc:subject>Apriori</dc:subject><dc:subject>Eclat</dc:subject><dc:subject>FP-growth</dc:subject><dc:description>One of the most popular methods of knowledge discovery in databases is the extraction of association rules. There are many different algorithms for association rule learning , which differ in space and time complexity. To perform a comparative analysis, we have implemented Apriori, Eclat and FP-growth algorithms and compared their time and memory consumption using synthetic and real databases. The analysis has shown that the FP-growth algorithm is the most efficient in the majority of cases.</dc:description><dc:publisher>[R. Akhmetshakirova]</dc:publisher><dc:date>2017</dc:date><dc:date>2017-02-14 12:16:38</dc:date><dc:type>Diplomsko delo/naloga</dc:type><dc:identifier>65060</dc:identifier><dc:language>sl</dc:language></rdf:Description></rdf:RDF>