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DTSTART:20180311T070000
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DTSTART;TZID=America/Toronto:20180924T133000
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URL:https://uwaterloo.ca/artificial-intelligence-group/events/masters-thesi
 s-presentation-computational-complexity-center
LOCATION:DC - William G. Davis Computer Research Centre 200 University Aven
 ue West 2310 Waterloo ON N2L 3G1 Canada
SUMMARY:Master’s Thesis Presentation: On the Computational Complexity of\
 nCenter-based Clustering
CLASS:PUBLIC
DESCRIPTION:NICOLE MCNABB\, MASTER’S CANDIDATE\n_David R. Cheriton School
  of Computer Science_\n\nClustering is the task of partitioning data so th
 at “similar”\npoints are grouped together and “dissimilar” ones ar
 e separated.\nIn general\, this is an ill-defined task. One way to make cl
 ustering\nwell-defined is to introduce a clustering objective to optimize.
  While\nmany common objectives such as k-means are known to be NP-hard\,\n
 heuristics output “nice” clustering solutions efficiently in\npractice
 . This work analyzes two avenues of theoretical research that\nattempt to 
 explain this discrepancy.
DTSTAMP:20260315T145035Z
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