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» Clustering with or without the Approximation
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STOC
2002
ACM
103views Algorithms» more  STOC 2002»
15 years 11 months ago
Approximate clustering via core-sets
In this paper, we show that for several clustering problems one can extract a small set of points, so that using those core-sets enable us to perform approximate clustering effici...
Mihai Badoiu, Sariel Har-Peled, Piotr Indyk
KDD
2007
ACM
159views Data Mining» more  KDD 2007»
15 years 11 months ago
Constraint-driven clustering
Clustering methods can be either data-driven or need-driven. Data-driven methods intend to discover the true structure of the underlying data while need-driven methods aims at org...
Rong Ge, Martin Ester, Wen Jin, Ian Davidson
JCP
2006
93views more  JCP 2006»
14 years 11 months ago
Reliable Partial Replication of Contents in Web Clusters: Getting Storage without losing Reliability
Traditionally, distributed Web servers have used two strategies for allocating files on server nodes: full replication and full distribution. While full replication provides a high...
José Daniel García, Jesús Car...
JMLR
2010
225views more  JMLR 2010»
14 years 6 months ago
Hartigan's Method: k-means Clustering without Voronoi
Hartigan's method for k-means clustering is the following greedy heuristic: select a point, and optimally reassign it. This paper develops two other formulations of the heuri...
Matus Telgarsky, Andrea Vattani
COMBINATORICA
2010
14 years 8 months ago
Set systems without a simplex or a cluster
Peter Keevash, Dhruv Mubayi