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» Modeling Relational Data as Graphs for Mining
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KDD
2009
ACM
180views Data Mining» more  KDD 2009»
16 years 5 months ago
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Sofus A. Macskassy
153
Voted
SYNASC
2006
IEEE
106views Algorithms» more  SYNASC 2006»
15 years 10 months ago
A Quality Measure for Multi-Level Community Structure
Mining relational data often boils down to computing clusters, that is finding sub-communities of data elements forming cohesive sub-units, while being well separated from one an...
Maylis Delest, Jean-Marc Fedou, Guy Melanço...
ICDM
2003
IEEE
118views Data Mining» more  ICDM 2003»
15 years 9 months ago
Links Between Kleinberg's Hubs and Authorities, Correspondence Analysis, and Markov Chains
In this work, we show that Kleinberg’s hubs and authorities model is closely related to both correspondence analysis, a well-known multivariate statistical technique, and a parti...
François Fouss, Marco Saerens, Jean-Michel ...
146
Voted
DATAMINE
1998
126views more  DATAMINE 1998»
15 years 4 months ago
An Extension to SQL for Mining Association Rules
Data mining evolved as a collection of applicative problems and efficient solution algorithms relative to rather peculiar problems, all focused on the discovery of relevant infor...
Rosa Meo, Giuseppe Psaila, Stefano Ceri
141
Voted
GECCO
2006
Springer
214views Optimization» more  GECCO 2006»
15 years 8 months ago
A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set
Many data mining applications involve the task of building a model for predictive classification. The goal of such a model is to classify examples (records or data instances) into...
Elon S. Correa, Alex Alves Freitas, Colin G. Johns...