Item-centric mining of frequent patterns from big uncertain data

Simple item page
dc.contributor.authorBraun, Peter
dc.contributor.authorCuzzocrea, Alfredo
dc.contributor.authorLeung, Carson
dc.contributor.authorPazdor, Adam G.M.
dc.contributor.authorSouza, Joglas
dc.date.accessioned2019-01-08T18:34:49Z
dc.date.available2019-01-08T18:34:49Z
dc.date.issued2018
dc.date.submitted2019-01-08T07:56:29Zen
dc.description.abstractHigh volumes of wide varieties of valuable data of different veracity (e.g., imprecise and uncertain data) can be easily generated or collected at a high velocity for various knowledge-based and intelligent information & engineering systems in many real-life situations. Embedded in these big data is valuable knowledge and useful information, which can be discovered by data science solutions. As a popular data science task, frequent pattern mining aims to discover implicit, previously unknown and potentially useful information and valuable knowledge in terms of sets of frequently co-occurring items. Many of the existing frequent pattern mining algorithms use a transaction-centric mining approach to find frequent patterns from precise data. However, there are situations in which an item-centric mining approach is more appropriate, and there are also situations in which data are imprecise and uncertain. In this article, we present an item-centric algorithm for mining frequent patterns from big uncertain data. Evaluation results show the effectiveness of our algorithm in item-centric mining of frequent patterns from big uncertain data.en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada (NSERC); University of Manitobaen_US
dc.identifier.citationP. Braun, A. Cuzzocrea, C.K. Leung, A.G.M. Pazdor, J. Souza. Item-centric mining of frequent patterns from big uncertain data. Procedia Computer Science, 126 (2018), pp. 1875-1884en_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.procs.2018.08.075
dc.identifier.urihttp://hdl.handle.net/1993/33659
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsopen accessen_US
dc.subjectbig dataen_US
dc.subjectdata analyticsen_US
dc.subjectdata miningen_US
dc.subjectdata scienceen_US
dc.subjectfrequent patternsen_US
dc.subjectimprecise dataen_US
dc.subjectuncertain dataen_US
dc.subjectknowledge discovery in databasesen_US
dc.subjectuncertaintyen_US
dc.subjectvertical miningen_US
dc.titleItem-centric mining of frequent patterns from big uncertain dataen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Braun_JProCS_126_2018.pdf
Size:
562.36 KB
Format:
Adobe Portable Document Format
Description:
P. Braun, A. Cuzzocrea, C.K. Leung, A.G.M. Pazdor, J. Souza. Item-centric mining of frequent patterns from big uncertain data. Procedia Computer Science, 126 (2018), pp. 1875-1884. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.24 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
University of Manitoba Scholarship
Faculty of Science Scholarly Works