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DTSTART:20190310T070000
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DTSTART:20181104T060000
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DTSTART;TZID=America/Toronto:20190327T163000
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URL:https://uwaterloo.ca/artificial-intelligence-group/events/phd-seminar-s
 emi-supervised-clustering-duplication
LOCATION:DC - William G. Davis Computer Research Centre 200 University Aven
 ue West 2310 Waterloo ON N2L 3G1 Canada
SUMMARY:PhD Seminar: Semi-supervised Clustering for De-duplication
CLASS:PUBLIC
DESCRIPTION:SHRINU KUSHAGRA\, PHD CANDIDATE\n_David R. Cheriton School of C
 omputer Science_\n\nData de-duplication is the task of detecting multiple 
 records that\ncorrespond to the same real-world entity in a database. In t
 his work\,\nwe view de-duplication as a clustering problem. We introduce a
 \nframework which we call promise correlation clustering. Given a\ncomplet
 e graph \\(G\\) with the edges labeled \\(0\\) and \\(1\\)\, the goal\nis 
 to find a clustering that minimizes the number of \\(0\\) edges\nwithin a 
 cluster plus the number of \\(1\\) edges across different\nclusters (or co
 rrelation loss). The optimal clustering can also be\nviewed as a complete 
 graph \\(G^*\\) with edges corresponding to points\nin the same cluster be
 ing labeled \\(0\\) and other edges being labeled\n\\(1\\).
DTSTAMP:20260324T064950Z
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