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195
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MLDM
2007
Springer
16 years 1 months ago
Transductive Learning from Relational Data
Transduction is an inference mechanism “from particular to particular”. Its application to classification tasks implies the use of both labeled (training) data and unlabeled (...
Michelangelo Ceci, Annalisa Appice, Nicola Barile,...
KDD
1998
ACM
140views Data Mining» more  KDD 1998»
15 years 11 months ago
Blurring the Distinction between Command and Data in Scientific KDD
We have been working on two different KDD systems for scientific data. One system involves comparative genomics, where the database contains more than 60,000 plant gene and protei...
John V. Carlis, Elizabeth Shoop, Scott Krieger
220
Voted
PKDD
2000
Springer
144views Data Mining» more  PKDD 2000»
15 years 10 months ago
Fast Hierarchical Clustering Based on Compressed Data and OPTICS
: One way to scale up clustering algorithms is to squash the data by some intelligent compression technique and cluster only the compressed data records. Such compressed data recor...
Markus M. Breunig, Hans-Peter Kriegel, Jörg S...
322
Voted
ICDE
2008
IEEE
137views Database» more  ICDE 2008»
16 years 8 months ago
Stop Chasing Trends: Discovering High Order Models in Evolving Data
Abstract-- Many applications are driven by evolving data -patterns in web traffic, program execution traces, network event logs, etc., are often non-stationary. Building prediction...
Shixi Chen, Haixun Wang, Shuigeng Zhou, Philip S. ...
KDD
2009
ACM
180views Data Mining» more  KDD 2009»
16 years 7 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