Postupci za učenje asocijativnih pravila

undergraduate thesis

• Title (english): Association Rules Learning Methods
• Author: Marin Smoljanić
• Mentor: Alan Jović (mentor)
• Committee member: Alan Jović (predsjednik povjerenstva)
• Committee member: Davor Kukolja (član povjerenstva)
• Committee member: Martin Soldić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2017-07-10, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Asocijativna pravila pogodna su za pronalaženje relacijskih odnosa između više varijabli u velikim skupovima podataka. Odnosi između varijabli, takozvana asocijativna pravila, izvode se u obliku implikacija AKO-ONDA.
Najčešće korišteni algoritmi generiranja asocijativnih pravila su Apriori, FP-Growth, Tertius, Eclat i OPUS.
U okviru ovog rada opisani su principi rada algoritama Apriori i FP-Growth te je opisan postupak implementacija algoritma Apriori unutar sustava za upravljanje podatcima imena Splunk.
U konačnici vlastita implementacija algoritma Apriori uspoređena je na više skupova podataka sa postojećom implementacijom istog algoritma u aplikaciji imena Weka.

Abstract (english)

The association rules are suitable for finding relationships between multiple variables in large sets of data. Relationships between variables, so-called association rules, are derived in the form of IF-THEN implications.
The most commonly used algorithms for generating association rules are Apriori, FP- Growth, Tertius, Eclat and OPUS.
This thesis describes the principles of Apriori and FP-Growth algorithms and also describes the procedure of Apriori algorithm implementation within Splunk data management system.
Results of Splunk implementation of the Apriori algorithm are compared to multiple data sets with the existing implementation of the same algorithm in the Weka application.

• Keywords: Asocijativna pravila; Apriori; FP-Growth; signifikantonst; pouzdanost; Weka; Splunk; Python
• Keywords (english): Association rules; Apriori; FP-Growth; support; support count; itemset; confidence; Weka; Splunk; Python
• Language: croatian
• URN:NBN: urn:nbn:hr:168:112804
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2019-03-14 00:48:41