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» Phenomenal Data Mining: From Data to Phenomena
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KDD
2009
ACM
224views Data Mining» more  KDD 2009»
15 years 6 months ago
Issues in evaluation of stream learning algorithms
Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that c...
João Gama, Raquel Sebastião, Pedro P...
SDM
2004
SIAM
165views Data Mining» more  SDM 2004»
15 years 2 months ago
Visualizing RFM Segmentation
Segmentation based on RFM (Recency, Frequency, and Monetary) has been used for over 50 years by direct marketers to target a subset of their customers, save mailing costs, and imp...
Ron Kohavi, Rajesh Parekh
KDD
2009
ACM
347views Data Mining» more  KDD 2009»
16 years 2 months ago
Network anomaly detection based on Eigen equation compression
This paper addresses the issue of unsupervised network anomaly detection. In recent years, networks have played more and more critical roles. Since their outages cause serious eco...
Shunsuke Hirose, Kenji Yamanishi, Takayuki Nakata,...
KDD
2000
ACM
97views Data Mining» more  KDD 2000»
15 years 5 months ago
Towards an effective cooperation of the user and the computer for classification
Decision trees have been successfully used for the task of classification. However, state-of-the-art algorithms do not incorporate the user in the tree construction process. This ...
Mihael Ankerst, Martin Ester, Hans-Peter Kriegel
KAIS
2008
221views more  KAIS 2008»
15 years 1 months ago
Forecasting skewed biased stochastic ozone days: analyses, solutions and beyond
Much work on skewed, stochastic, high dimensional, and biased datasets usually implicitly solve each problem separately. Recently, we have been approached by Texas Commission on En...
Kun Zhang, Wei Fan