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dc.rights.licenseCC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.
dc.contributorGoldberg, Mark
dc.contributor.authorWillmore, Christopher P.
dc.date.accessioned2021-11-03T07:46:00Z
dc.date.available2021-11-03T07:46:00Z
dc.date.created2008-04-28T14:03:59Z
dc.date.issued2008-05
dc.identifier.urihttps://hdl.handle.net/20.500.13015/527
dc.descriptionMay 2008
dc.descriptionSchool of Science
dc.description.abstractThis thesis describes a new two-step algorithm for finding hidden groups from chat transcripts, that is, transcripts of communication where the recipient of a message is not known. The algorithm is presented in two steps: calculating a correlation value between every pair of users in the chat transcript, and finding clusters in the weighted undirected graph that results. The inter-user correlation can be calculated in a number of different ways, some of which are accomplished by projecting individual user transcripts into an inner product space. The clustering step uses the existing iterative-scan algorithm, with some new modifications. This approach is found to work under limited conditions.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectComputer science
dc.titleTimestamp-based correlation measures for finding hidden groups in chat rooms
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid10904
dc.digitool.pid10905
dc.digitool.pid10907
dc.digitool.pid10906
dc.digitool.pid10908
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreeMS
dc.relation.departmentDept. of Computer Science


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CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.
Except where otherwise noted, this item's license is described as CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.