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KDD
2006
ACM
164views Data Mining» more  KDD 2006»
15 years 10 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
KDD
2003
ACM
161views Data Mining» more  KDD 2003»
15 years 10 months ago
Empirical Bayesian data mining for discovering patterns in post-marketing drug safety
Because of practical limits in characterizing the safety profiles of therapeutic products prior to marketing, manufacturers and regulatory agencies perform post-marketing surveill...
David M. Fram, June S. Almenoff, William DuMouchel
KDD
2003
ACM
109views Data Mining» more  KDD 2003»
15 years 10 months ago
Experimental design for solicitation campaigns
Data mining techniques are routinely used by fundraisers to select those prospects from a large pool of candidates who are most likely to make a financial contribution. These tech...
Uwe F. Mayer, Armand Sarkissian
KDD
2002
ACM
112views Data Mining» more  KDD 2002»
15 years 10 months ago
From run-time behavior to usage scenarios: an interaction-pattern mining approach
A key challenge facing IT organizations today is their evolution towards adopting e-business practices that gives rise to the need for reengineering their underlying software syst...
Mohammad El-Ramly, Eleni Stroulia, Paul G. Sorenso...
KDD
2001
ACM
163views Data Mining» more  KDD 2001»
15 years 10 months ago
The "DGX" distribution for mining massive, skewed data
Skewed distributions appear very often in practice. Unfortunately, the traditional Zipf distribution often fails to model them well. In this paper, we propose a new probability di...
Zhiqiang Bi, Christos Faloutsos, Flip Korn
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