dc.contributor.author |
Peng, AY |
en |
dc.contributor.author |
Koh, Yun Sing |
en |
dc.contributor.author |
Riddle, Patricia |
en |
dc.contributor.editor |
Kim, J |
en |
dc.contributor.editor |
Shim, K |
en |
dc.contributor.editor |
Cao, L |
en |
dc.contributor.editor |
Lee, JG |
en |
dc.contributor.editor |
Lin, X |
en |
dc.contributor.editor |
Moon, YS |
en |
dc.coverage.spatial |
Jeju, South Korea |
en |
dc.date.accessioned |
2018-10-04T00:49:22Z |
en |
dc.date.issued |
2017 |
en |
dc.identifier.citation |
PAKDD 2017: 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Jeju, South Korea, 23 May 2017 - 26 May 2017. Editors: Kim J, Shim K, Cao L, Lee JG, Lin X, Moon YS. Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia Conference, PAKDD 2017 (Lecture Notes in Computer Science). 10235 : 196-207. |
en |
dc.identifier.isbn |
9783319575285 |
en |
dc.identifier.issn |
0302-9743 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/38686 |
en |
dc.description.abstract |
High utility itemset mining is the problem of finding sets of items whose utilities are higher than or equal to a specific threshold. We propose a novel technique called mHUIMiner, which utilises a tree structure to guide the itemset expansion process to avoid considering itemsets that are nonexistent in the database. Unlike current techniques, it does not have a complex pruning strategy that requires expensive computation overhead. Extensive experiments have been done to compare mHUIMiner to other state-of-the-art algorithms. The experimental results show that our technique outperforms the state-of-the-art algorithms in terms of running time for sparse datasets. |
en |
dc.publisher |
Springer Verlag |
en |
dc.relation.ispartof |
PAKDD 2017: 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining |
en |
dc.relation.ispartofseries |
Lecture Notes in Computer Science: PAKDD 2017: Advances in Knowledge Discovery and Data Mining |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights |
This is a post-peer-review, pre-copyedit version of an article published in Lecture notes in Computer Science. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-319-57529-2_16 |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.rights.uri |
https://www.springer.com/gb/open-access/authors-rights/self-archiving-policy/2124 |
en |
dc.title |
mHUIMiner: A Fast High Utility Itemset Mining Algorithm for Sparse Datasets |
en |
dc.type |
Conference Item |
en |
dc.identifier.doi |
10.1007/978-3-319-57529-2_16 |
en |
pubs.begin-page |
196 |
en |
pubs.volume |
LNCS 10235 |
en |
dc.rights.holder |
Copyright: Springer |
en |
pubs.end-page |
207 |
en |
pubs.finish-date |
2017-05-26 |
en |
pubs.start-date |
2017-05-23 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
Proceedings |
en |
pubs.elements-id |
624559 |
en |
pubs.org-id |
Science |
en |
pubs.org-id |
School of Computer Science |
en |
dc.identifier.eissn |
1611-3349 |
en |
pubs.record-created-at-source-date |
2017-05-08 |
en |