Online k-means Clustering on Arbitrary Data Streams - Lecture

Online k-means Clustering on Arbitrary Data Streams - Lecture

USC Probability and Statistics Seminar via YouTube Direct link

Intro

1 of 16

1 of 16

Intro

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Online k-means Clustering on Arbitrary Data Streams - Lecture

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  1. 1 Intro
  2. 2 k-means clustering
  3. 3 The online setting
  4. 4 The Goal(s) Cohen-Addad et. al. 2021
  5. 5 A troubling example
  6. 6 Lower Bound
  7. 7 A natural starting point: streaming
  8. 8 A difficult case for streaming
  9. 9 Idea 1: don't remove centers
  10. 10 Proof Sketch
  11. 11 We still have problems on pathological examples.
  12. 12 Idea 2: Using the scale to delete points.
  13. 13 The Lemma Revisited
  14. 14 Our Algorithm's Performance
  15. 15 Proof idea
  16. 16 Future Directions

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