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dc.contributor.authorYavaş G.en_US
dc.contributor.authorKatsaros, D.en_US
dc.contributor.authorUlusoy, Ö.en_US
dc.contributor.authorManolopoulos, Y.en_US
dc.date.accessioned2016-02-08T10:22:49Z
dc.date.available2016-02-08T10:22:49Zen_US
dc.date.issued2005en_US
dc.identifier.issn1872-6933
dc.identifier.issn0169-023X
dc.identifier.urihttp://hdl.handle.net/11693/24015
dc.description.abstractMobility prediction is one of the most essential issues that need to be explored for mobility management in mobile computing systems. In this paper, we propose a new algorithm for predicting the next inter-cell movement of a mobile user in a Personal Communication Systems network. In the first phase of our three-phase algorithm, user mobility patterns are mined from the history of mobile user trajectories. In the second phase, mobility rules are extracted from these patterns, and in the last phase, mobility predictions are accomplished by using these rules. The performance of the proposed algorithm is evaluated through simulation as compared to two other prediction methods. The performance results obtained in terms of Precision and Recall indicate that our method can make more accurate predictions than the other methods. © 2004 Elsevier B.V. All rights reserved.en_US
dc.language.isoEnglishen_US
dc.source.titleData and Knowledge Engineeringen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.datak.2004.09.004en_US
dc.subjectData miningen_US
dc.subjectLocation predictionen_US
dc.subjectMobile computingen_US
dc.subjectMobility patternsen_US
dc.subjectMobility predictionen_US
dc.subjectAlgorithmsen_US
dc.subjectData miningen_US
dc.subjectData processingen_US
dc.subjectGlobal positioning systemen_US
dc.subjectKnowledge engineeringen_US
dc.subjectPersonal communication systemsen_US
dc.subjectProbabilityen_US
dc.subjectResource allocationen_US
dc.subjectLocation predictionen_US
dc.subjectMobile user trajectoriesen_US
dc.subjectMobility patternsen_US
dc.subjectMobility predictionsen_US
dc.subjectMobile computingen_US
dc.titleA data mining approach for location prediction in mobile environmentsen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage121en_US
dc.citation.epage146en_US
dc.citation.volumeNumber54en_US
dc.citation.issueNumber2en_US
dc.identifier.doi10.1016/j.datak.2004.09.004en_US
dc.publisherElsevieren_US


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