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Article: Efficient mining of frequent item sets on large uncertain databases

TitleEfficient mining of frequent item sets on large uncertain databases
Authors
KeywordsApproximate algorithm
Frequent item sets
Incremental mining
Uncertain data set
Database systems
Issue Date2012
PublisherIEEE. The Journal's web site is located at http://www.computer.org/tkde
Citation
IEEE Transactions on Knowledge & Data Engineering, 2012, v. 24 n. 12, p. 2170-2183 How to Cite?
AbstractThe data handled in emerging applications like location-based services, sensor monitoring systems, and data integration, are often inexact in nature. In this paper, we study the important problem of extracting frequent item sets from a large uncertain database, interpreted under the Possible World Semantics (PWS). This issue is technically challenging, since an uncertain database contains an exponential number of possible worlds. By observing that the mining process can be modeled as a Poisson binomial distribution, we develop an approximate algorithm, which can efficiently and accurately discover frequent item sets in a large uncertain database. We also study the important issue of maintaining the mining result for a database that is evolving (e.g., by inserting a tuple). Specifically, we propose incremental mining algorithms, which enable Probabilistic Frequent Item set (PFI) results to be refreshed. This reduces the need of re-executing the whole mining algorithm on the new database, which is often more expensive and unnecessary. We examine how an existing algorithm that extracts exact item sets, as well as our approximate algorithm, can support incremental mining. All our approaches support both tuple and attribute uncertainty, which are two common uncertain database models. We also perform extensive evaluation on real and synthetic data sets to validate our approaches. © 1989-2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/138034
ISSN
2015 Impact Factor: 2.476
2015 SCImago Journal Rankings: 2.087
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Len_US
dc.contributor.authorCheung, DWLen_US
dc.contributor.authorCheng, Ren_US
dc.contributor.authorLee, SDen_US
dc.contributor.authorYang, XS-
dc.date.accessioned2011-08-26T14:39:02Z-
dc.date.available2011-08-26T14:39:02Z-
dc.date.issued2012en_US
dc.identifier.citationIEEE Transactions on Knowledge & Data Engineering, 2012, v. 24 n. 12, p. 2170-2183en_US
dc.identifier.issn1041-4347-
dc.identifier.urihttp://hdl.handle.net/10722/138034-
dc.description.abstractThe data handled in emerging applications like location-based services, sensor monitoring systems, and data integration, are often inexact in nature. In this paper, we study the important problem of extracting frequent item sets from a large uncertain database, interpreted under the Possible World Semantics (PWS). This issue is technically challenging, since an uncertain database contains an exponential number of possible worlds. By observing that the mining process can be modeled as a Poisson binomial distribution, we develop an approximate algorithm, which can efficiently and accurately discover frequent item sets in a large uncertain database. We also study the important issue of maintaining the mining result for a database that is evolving (e.g., by inserting a tuple). Specifically, we propose incremental mining algorithms, which enable Probabilistic Frequent Item set (PFI) results to be refreshed. This reduces the need of re-executing the whole mining algorithm on the new database, which is often more expensive and unnecessary. We examine how an existing algorithm that extracts exact item sets, as well as our approximate algorithm, can support incremental mining. All our approaches support both tuple and attribute uncertainty, which are two common uncertain database models. We also perform extensive evaluation on real and synthetic data sets to validate our approaches. © 1989-2012 IEEE.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://www.computer.org/tkde-
dc.relation.ispartofIEEE Transactions on Knowledge & Data Engineeringen_US
dc.rightsIEEE Transactions on Knowledge & Data Engineering. Copyright © IEEE.-
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectApproximate algorithm-
dc.subjectFrequent item sets-
dc.subjectIncremental mining-
dc.subjectUncertain data set-
dc.subjectDatabase systems-
dc.titleEfficient mining of frequent item sets on large uncertain databasesen_US
dc.typeArticleen_US
dc.identifier.emailWang, L: lwang@cs.hku.hken_US
dc.identifier.emailCheung, DWL: dcheung@cs.hku.hken_US
dc.identifier.emailCheng, RCK: ckcheng@cs.hku.hken_US
dc.identifier.emailLee, SD: sdlee@cs.hku.hk-
dc.identifier.emailYang, XS: xyang2@cs.hku.hk-
dc.identifier.authorityCheung, DWL=rp00101en_US
dc.identifier.authorityCheng, RCK=rp00074en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TKDE.2011.165-
dc.identifier.scopuseid_2-s2.0-84867943010-
dc.identifier.hkuros190763en_US
dc.identifier.volume24-
dc.identifier.issue12-
dc.identifier.spage2170-
dc.identifier.epage2183-
dc.identifier.isiWOS:000309914400005-
dc.publisher.placeUnited States-
dc.identifier.citeulike11273187-

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