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Algorithms for association rule learning
Renata Akhmetshakirova, 2017, diplomsko delo

Opis: 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.
Ključne besede: association rules, data mining, Apriori, Eclat, FP-growth
Objavljeno: 24.02.2017; Ogledov: 986; Prenosov: 68
.pdf Celotno besedilo (1,17 MB)

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