Please use this identifier to cite or link to this item:
https://hdl.handle.net/11147/2709
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yıldız, Barış | - |
dc.contributor.author | Ergenç, Belgin | - |
dc.date.accessioned | 2017-01-04T07:19:51Z | |
dc.date.available | 2017-01-04T07:19:51Z | |
dc.date.issued | 2010 | |
dc.identifier.citation | Yıldız, B., and Ergenç, B. (2010). Comparison of two association rule mining algorithms without candidate generation. Paper presented at the 10th IASTED International Conference on Artificial Intelligence and Applications, AIA 2010; Innsbruck; Austria; 15-17 February (450-457). | en_US |
dc.identifier.isbn | 9780889868182 | |
dc.identifier.uri | http://hdl.handle.net/11147/2709 | |
dc.description.abstract | Association rule mining techniques play an important role in data mining research where the aim is to find interesting correlations among sets of items in databases. Although the Apriori algorithm of association rule mining is the one that boosted data mining research, it has a bottleneck in its candidate generation phase that requires multiple passes over the source data. FP-Growth and Matrix Apriori are two algorithms that overcome that bottleneck by keeping the frequent itemsets in compact data structures, eliminating the need of candidate generation. To our knowledge, there is no work to compare those two similar algorithms focusing on their performances in different phases of execution. In this study, we compare Matrix Apriori and FP-Growth algorithms. Two case studies analyzing the algorithms are carried out phase by phase using two synthetic datasets generated in order i) to see their performance with datasets having different characteristics, ii) to understand the causes of performance differences in different phases. Our findings are i) performances of algorithms are related to the characteristics of the given dataset and threshold value, ii) Matrix Apriori outperforms FP-Growth in total performance for threshold values below 10%, iii) although building matrix data structure has higher cost, finding itemsets is faster. | en_US |
dc.language.iso | en | en_US |
dc.publisher | ACTA Press | en_US |
dc.relation.ispartof | 10th IASTED International Conference on Artificial Intelligence and Applications, AIA 2010 | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Data mining | en_US |
dc.subject | Association rule mining | en_US |
dc.subject | Matrix apriori | en_US |
dc.subject | FP-growth algorithm | en_US |
dc.title | Comparison of two association rule mining algorithms without candidate generation | en_US |
dc.type | Conference Object | en_US |
dc.authorid | TR130596 | en_US |
dc.institutionauthor | Yıldız, Barış | - |
dc.institutionauthor | Ergenç, Belgin | - |
dc.department | İzmir Institute of Technology. Computer Engineering | en_US |
dc.identifier.startpage | 450 | en_US |
dc.identifier.endpage | 457 | en_US |
dc.identifier.scopus | 2-s2.0-77954602647 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.identifier.wosquality | N/A | - |
dc.identifier.scopusquality | N/A | - |
item.languageiso639-1 | en | - |
item.openairetype | Conference Object | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 03.04. Department of Computer Engineering | - |
Appears in Collections: | Computer Engineering / Bilgisayar Mühendisliği Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
CORE Recommender
SCOPUSTM
Citations
16
checked on Sep 1, 2024
Page view(s)
496
checked on Sep 2, 2024
Download(s)
884
checked on Sep 2, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.