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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp011v53k041f
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dc.contributor.advisorKpotufe, Samory-
dc.contributor.authorDavis, Erick-
dc.date.accessioned2016-06-24T13:51:27Z-
dc.date.available2016-06-24T13:51:27Z-
dc.date.created2016-04-12-
dc.date.issued2016-06-24-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp011v53k041f-
dc.description.abstractWe present a review of outlier detection techniques and attempt to find a consensus approach for defining an outlier. We then extensively explore device activity in smart home networks. We attempt to identify individual device network profiles and differentiate the behavior originating from device activities. This is formulated as a clustering problem. Device behavior is analyzed in the context of finding an appropriate outlier detection definition and approach.en_US
dc.format.extent96 pages*
dc.language.isoen_USen_US
dc.titleClustering and Outlier Detection: Methods and Applications in Smart Home Networksen_US
dc.typePrinceton University Senior Theses-
pu.date.classyear2016en_US
pu.departmentOperations Research and Financial Engineeringen_US
pu.pdf.coverpageSeniorThesisCoverPage-
Appears in Collections:Operations Research and Financial Engineering, 2000-2020

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