Show simple item record

dc.contributor.authorVanBuren, Victoria ( )en_US
dc.contributor.authorVillarreal, David ( )en_US
dc.contributor.authorMcMillen, Thomas A. ( )en_US
dc.contributor.authorMinnicks, Andrew L. ( )en_US
dc.date.accessioned2009-10-14T10:03:29Z
dc.date.available2012-02-24T10:03:31Z
dc.date.issued2009-09-25en_US
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/2583
dc.descriptionReport Number TXSTATE-CS-TR-2009-12. Research advisor: Professor Wilbon Davis.en_US
dc.description.abstractThis paper discusses a probabilistic approach to address the problem of searching through large amount of data to find case-relevant documents. Using a valuable collection of data, e-mail communications from Enron, an actual corporation, we train a Bayes-based text classifier algorithm to identify e-mails known to be case-relevant and those known to be case-irrelevant.en_US
dc.formatText
dc.format.extent16 pages
dc.format.medium1 file (.pdf)
dc.language.isoen
dc.subjectEnron dataseten_US
dc.subjectE-mail Relevanceen_US
dc.subjectE-mail classificationen_US
dc.subjectBayes classifieren_US
dc.subjectElectronic discoveryen_US
dc.subjectForensicsen_US
dc.subject.classificationComputer Sciencesen_US
dc.titleEnron Dataset Research: E-mail Relevance Classificationen_US
txstate.documenttypeTechnical Report
txstate.departmentComputer Science


Download

Thumbnail

This item appears in the following Collection(s)

Show simple item record