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» Practical Preference Relations for Large Data Sets
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KDD
2006
ACM
164views Data Mining» more  KDD 2006»
14 years 8 months ago
Sampling from large graphs
Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.)...
Jure Leskovec, Christos Faloutsos
IAAI
2003
13 years 9 months ago
LAW: A Workbench for Approximate Pattern Matching in Relational Data
Pattern matching for intelligence organizations is a challenging problem. The data sets are large and noisy, and there is a flexible and constantly changing notion of what consti...
Michael Wolverton, Pauline Berry, Ian W. Harrison,...
KDD
2000
ACM
149views Data Mining» more  KDD 2000»
14 years 10 hour ago
Efficient clustering of high-dimensional data sets with application to reference matching
Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques f...
Andrew McCallum, Kamal Nigam, Lyle H. Ungar
VLDB
2004
ACM
120views Database» more  VLDB 2004»
14 years 1 months ago
Relational link-based ranking
Link analysis methods show that the interconnections between web pages have lots of valuable information. The link analysis methods are, however, inherently oriented towards analy...
Floris Geerts, Heikki Mannila, Evimaria Terzi
IPPS
1996
IEEE
14 years 18 days ago
Practical Parallel Algorithms for Dynamic Data Redistribution, Median Finding, and Selection
A common statistical problem is that of nding the median element in a set of data. This paper presents a fastand portable parallel algorithm for nding the median given a set of el...
David A. Bader, Joseph JáJá