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» Approximate Distance Oracles for Graphs with Dense Clusters
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APPROX
2006
Springer
120views Algorithms» more  APPROX 2006»
13 years 9 months ago
Approximating Average Parameters of Graphs
Inspired by Feige (36th STOC, 2004), we initiate a study of sublinear randomized algorithms for approximating average parameters of a graph. Specifically, we consider the average ...
Oded Goldreich, Dana Ron
PODC
2006
ACM
13 years 11 months ago
Object location using path separators
We study a novel separator property called k-path separable. Roughly speaking, a k-path separable graph can be recursively separated into smaller components by sequentially removi...
Ittai Abraham, Cyril Gavoille
IJCV
2007
115views more  IJCV 2007»
13 years 5 months ago
Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering
This paper describes work aimed at the unsupervised learning of shape-classes from shock trees. We commence by considering how to compute the edit distance between weighted trees. ...
Andrea Torsello, Antonio Robles-Kelly, Edwin R. Ha...
KDD
2004
ACM
137views Data Mining» more  KDD 2004»
13 years 10 months ago
Mining scale-free networks using geodesic clustering
Many real-world graphs have been shown to be scale-free— vertex degrees follow power law distributions, vertices tend to cluster, and the average length of all shortest paths is...
Andrew Y. Wu, Michael Garland, Jiawei Han
STACS
2007
Springer
13 years 11 months ago
Small Space Representations for Metric Min-Sum k -Clustering and Their Applications
The min-sum k-clustering problem is to partition a metric space (P, d) into k clusters C1, . . . , Ck ⊆ P such that k i=1 p,q∈Ci d(p, q) is minimized. We show the first effi...
Artur Czumaj, Christian Sohler