| Naslov: | Algorithms for association rule learning |
|---|
| Avtorji: | ID Akhmetshakirova, Renata (Avtor)
ID Strnad, Damjan (Mentor) Več o mentorju...  |
|---|
| Datoteke: |  UN_Akhmetshakirova_Renata_2017.pdf (1,17 MB)
MD5: F9964F5C67BE30AAB7203587C513D0EC
|
|---|
| Jezik: | Angleški jezik |
|---|
| Vrsta gradiva: | Diplomsko delo/naloga |
|---|
| Tipologija: | 2.11 - Diplomsko delo |
|---|
| Organizacija: | FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
|
|---|
| 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 |
|---|
| Kraj izida: | Maribor |
|---|
| Založnik: | [R. Akhmetshakirova] |
|---|
| Leto izida: | 2017 |
|---|
| PID: | 20.500.12556/DKUM-65060 |
|---|
| UDK: | 004.85.021(043.2) |
|---|
| COBISS.SI-ID: | 20432150 |
|---|
| NUK URN: | URN:SI:UM:DK:6PWJCETP |
|---|
| Datum objave v DKUM: | 24.02.2017 |
|---|
| Število ogledov: | 2590 |
|---|
| Število prenosov: | 127 |
|---|
| Metapodatki: |    |
|---|
| Področja: | KTFMB - FERI
|
|---|
|
:
|
Kopiraj citat |
|---|
| | | | Skupna ocena: | (0 glasov) |
|---|
| Vaša ocena: | Ocenjevanje je dovoljeno samo prijavljenim uporabnikom. |
|---|
| Objavi na: |  |
|---|
Postavite miškin kazalec na naslov za izpis povzetka. Klik na naslov izpiše
podrobnosti ali sproži prenos. |
