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1
Introduction
2
Structure of computational problems
3
Clustering
4
Continuous Clustering
5
Clustering is a Hard Problem
6
Approximation Algorithms
7
Hardness of Approximation
8
Results
9
Proof
10
Vertex Edge Game
11
Randomness
12
Graph embedding
13
Key takeaways
14
Johnson Coverage Hypothesis
15
In Approximability
16
Conclusion
17
Questions
Description:
Explore the complexities of clustering algorithms in Lp metrics through this 21-minute IEEE conference talk. Delve into the structure of computational problems, focusing on continuous clustering and its inherent difficulties. Examine approximation algorithms and the hardness of approximation in this context. Learn about key concepts such as the Vertex Edge Game, randomness, and graph embedding. Understand the significance of the Johnson Coverage Hypothesis in inapproximability. Gain valuable insights into the challenges of clustering problems and their implications in computational complexity theory.

Inapproximability of Clustering in Lp Metrics

IEEE
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