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KDD
2006
ACM
145views Data Mining» more  KDD 2006»
14 years 8 months ago
Deriving quantitative models for correlation clusters
Correlation clustering aims at grouping the data set into correlation clusters such that the objects in the same cluster exhibit a certain density and are all associated to a comm...
Arthur Zimek, Christian Böhm, Elke Achtert, H...
KDD
2006
ACM
240views Data Mining» more  KDD 2006»
14 years 8 months ago
Adaptive event detection with time-varying poisson processes
Time-series of count data are generated in many different contexts, such as web access logging, freeway traffic monitoring, and security logs associated with buildings. Since this...
Alexander T. Ihler, Jon Hutchins, Padhraic Smyth
KDD
2004
ACM
139views Data Mining» more  KDD 2004»
14 years 8 months ago
Learning a complex metabolomic dataset using random forests and support vector machines
Metabolomics is the omics science of biochemistry. The associated data include the quantitative measurements of all small molecule metabolites in a biological sample. These datase...
Young Truong, Xiaodong Lin, Chris Beecher
KDD
2002
ACM
196views Data Mining» more  KDD 2002»
14 years 8 months ago
Comparing Two Recommender Algorithms with the Help of Recommendations by Peers
Abstract. Since more and more Web sites, especially sites of retailers, offer automatic recommendation services using Web usage mining, evaluation of recommender algorithms has bec...
Andreas Geyer-Schulz, Michael Hahsler
PODS
2008
ACM
159views Database» more  PODS 2008»
14 years 7 months ago
Approximation algorithms for clustering uncertain data
There is an increasing quantity of data with uncertainty arising from applications such as sensor network measurements, record linkage, and as output of mining algorithms. This un...
Graham Cormode, Andrew McGregor