| Title: | Algorithms for association rule learning |
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| Authors: | ID Akhmetshakirova, Renata (Author)
ID Strnad, Damjan (Mentor) More about this mentor...  |
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| Files: |  UN_Akhmetshakirova_Renata_2017.pdf (1,17 MB)
MD5: F9964F5C67BE30AAB7203587C513D0EC
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| Language: | English |
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| Work type: | Bachelor thesis/paper |
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| Typology: | 2.11 - Undergraduate Thesis |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | 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. |
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| Keywords: | association rules, data mining, Apriori, Eclat, FP-growth |
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| Place of publishing: | Maribor |
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| Publisher: | [R. Akhmetshakirova] |
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| Year of publishing: | 2017 |
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| PID: | 20.500.12556/DKUM-65060 |
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| UDC: | 004.85.021(043.2) |
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| COBISS.SI-ID: | 20432150 |
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| NUK URN: | URN:SI:UM:DK:6PWJCETP |
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| Publication date in DKUM: | 24.02.2017 |
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| Views: | 2587 |
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| Downloads: | 127 |
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| Metadata: |    |
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| Categories: | KTFMB - FERI
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