Please use this identifier to cite or link to this item:
http://arks.princeton.edu/ark:/88435/dsp011v53k041f
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Kpotufe, Samory | - |
dc.contributor.author | Davis, Erick | - |
dc.date.accessioned | 2016-06-24T13:51:27Z | - |
dc.date.available | 2016-06-24T13:51:27Z | - |
dc.date.created | 2016-04-12 | - |
dc.date.issued | 2016-06-24 | - |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/dsp011v53k041f | - |
dc.description.abstract | We 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.extent | 96 pages | * |
dc.language.iso | en_US | en_US |
dc.title | Clustering and Outlier Detection: Methods and Applications in Smart Home Networks | en_US |
dc.type | Princeton University Senior Theses | - |
pu.date.classyear | 2016 | en_US |
pu.department | Operations Research and Financial Engineering | en_US |
pu.pdf.coverpage | SeniorThesisCoverPage | - |
Appears in Collections: | Operations Research and Financial Engineering, 2000-2020 |
Files in This Item:
File | Size | Format | |
---|---|---|---|
Davis_Erik_Thesis.pdf | 1.71 MB | Adobe PDF | Request a copy |
Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.