In this thesis we consider the problem of embedding a graph metric into a tree metric so that the distances in graph are closely resembled by the distances in the tree . We modify a known best algorithm for the problem and analyze its performance on large real-life networks. Our experimentation shows that many real-life networks are embeddable into trees with very small distortions. These low distortion embeddings can be used to obtain good approximate solutions to many problems related to distances in graph. We demonstrate this on the well-known min-max clustering problem.
|Commitee:||Dragan, Feodor F., Jin, Ruoming, Maletic, Jonathan I.|
|School:||Kent State University|
|Department:||College of Arts and Sciences / Department of Computer Science|
|School Location:||United States -- Ohio|
|Source:||MAI 56/05M(E), Masters Abstracts International|
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