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 language | English |
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Title of host publication | Computer Aided Systems Theory, EUROCAST 2011 - 13th International Conference, Revised Selected Papers |
Pages | 464-471 |
Number of pages | 8 |
Volume | 6927 |
Edition | PART 1 |
DOIs | |
Publication status | Published - 2012 |
Event | 13th International Conference on Computer Aided Systems Theory, Eurocast 2011 - Las Palmas de Gran Canaria, Spain Duration: 6 Feb 2011 → 11 Feb 2011 http://www.iuctc.ulpgc.es/spain/eurocast2011/ |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Number | PART 1 |
Volume | 6927 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 13th International Conference on Computer Aided Systems Theory, Eurocast 2011 |
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Country/Territory | Spain |
City | Las Palmas de Gran Canaria |
Period | 06.02.2011 → 11.02.2011 |
Internet address |