Algorithms for association rule learning

thesis work

Renata Akhmetshakirova (Author), Damjan Strnad (Mentor)

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.

Keywords

association rules;data mining;Apriori;Eclat;

Data

Language:	English
Year of publishing:	2017
Typology:	2.11 - Undergraduate Thesis
Organization:	UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher:	[R. Akhmetshakirova]
UDC:	004.85.021(043.2)
COBISS:	20432150
Views:	1721
Downloads:	93
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary title:	Algoritmi učenja asociacijskih pravil
Secondary abstract:	Zanimanje za metode odkrivanja znanja v podatkovnih bazah nenehno raste. Sodobne podatkovne baze so zelo velike, dosegajo terabajte in težijo k nadaljnemu povečanju, kar zahteva učinkovite, razširljive algoritme, ki lahko rešujejo težavo obdelave podatkov v razumnem roku. Ena od najbolj učinkovitih in priljubljenih metod za odkrivanje znanja je učenje asociacijskih pravil za ugotovitev različnih vrst pravilnosti v podatkih.
Secondary keywords:	asociacijska pravila;podatkovno rudarjenje;Apriori;Eclat;FP-growth;diplomske naloge;
URN:	URN:SI:UM:
Type (COBISS):	Bachelor thesis/paper
Thesis comment:	Univ. v Mariboru, Fak. za elektrotehniko, računalništvo in informatiko, Računalništvo in informacijske tehnologije
Pages:	VIII, 32 f.
ID:	9577802