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CORR
2006
Springer
151views Education» more  CORR 2006»
13 years 7 months ago
Graph Laplacians and their convergence on random neighborhood graphs
Given a sample from a probability measure with support on a submanifold in Euclidean space one can construct a neighborhood graph which can be seen as an approximation of the subm...
Matthias Hein, Jean-Yves Audibert, Ulrike von Luxb...
APPROX
2008
Springer
101views Algorithms» more  APPROX 2008»
13 years 9 months ago
Streaming Algorithms for k-Center Clustering with Outliers and with Anonymity
Clustering is a common problem in the analysis of large data sets. Streaming algorithms, which make a single pass over the data set using small working memory and produce a cluster...
Richard Matthew McCutchen, Samir Khuller
SODA
2008
ACM
200views Algorithms» more  SODA 2008»
13 years 9 months ago
Clustering for metric and non-metric distance measures
We study a generalization of the k-median problem with respect to an arbitrary dissimilarity measure D. Given a finite set P, our goal is to find a set C of size k such that the s...
Marcel R. Ackermann, Johannes Blömer, Christi...
KDD
2012
ACM
196views Data Mining» more  KDD 2012»
11 years 10 months ago
Chromatic correlation clustering
We study a novel clustering problem in which the pairwise relations between objects are categorical. This problem can be viewed as clustering the vertices of a graph whose edges a...
Francesco Bonchi, Aristides Gionis, Francesco Gull...
SIGMOD
2001
ACM
200views Database» more  SIGMOD 2001»
14 years 7 months ago
Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering
In this paper, we investigate how to scale hierarchical clustering methods (such as OPTICS) to extremely large databases by utilizing data compression methods (such as BIRCH or ra...
Markus M. Breunig, Hans-Peter Kriegel, Peer Kr&oum...