Market basket analysis of retail data: Supervised learning approach

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

2 Citations (Scopus)

Abstract

In this work we discuss a supervised learning approach for identification of frequent itemsets and association rules from transactional data. This task is typically encountered in market basket analysis, where the goal is to find subsets of products that are frequently purchased in combination. In this work we compare the traditional approach and the supervised learning approach to find association rules in a real-world retail data set using two well known algorithm, namely Apriori and PRIM.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory, EUROCAST 2011 - 13th International Conference, Revised Selected Papers
Pages464-471
Number of pages8
Volume6927
EditionPART 1
DOIs
Publication statusPublished - 2012
Event13th International Conference on Computer Aided Systems Theory, Eurocast 2011 - Las Palmas de Gran Canaria, Spain
Duration: 6 Feb 201111 Feb 2011
http://www.iuctc.ulpgc.es/spain/eurocast2011/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6927 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Computer Aided Systems Theory, Eurocast 2011
CountrySpain
CityLas Palmas de Gran Canaria
Period06.02.201111.02.2011
Internet address

Fingerprint Dive into the research topics of 'Market basket analysis of retail data: Supervised learning approach'. Together they form a unique fingerprint.

Cite this