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195
Voted
KDD
2006
ACM
145views Data Mining» more  KDD 2006»
16 years 7 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...
208
Voted
SIGIR
2012
ACM
13 years 9 months ago
Improving searcher models using mouse cursor activity
Web search components such as ranking and query suggestions analyze the user data provided in query and click logs. While this data is easy to collect and provides information abo...
Jeff Huang, Ryen W. White, Georg Buscher, Kuansan ...
215
Voted
KDD
2005
ACM
161views Data Mining» more  KDD 2005»
16 years 7 months ago
Combining email models for false positive reduction
Machine learning and data mining can be effectively used to model, classify and discover interesting information for a wide variety of data including email. The Email Mining Toolk...
Shlomo Hershkop, Salvatore J. Stolfo
197
Voted
KDD
2004
ACM
179views Data Mining» more  KDD 2004»
16 years 7 months ago
1-dimensional splines as building blocks for improving accuracy of risk outcomes models
Transformation of both the response variable and the predictors is commonly used in fitting regression models. However, these transformation methods do not always provide the maxi...
David S. Vogel, Morgan C. Wang
224
Voted
KDD
2003
ACM
191views Data Mining» more  KDD 2003»
16 years 7 months ago
Assessment and pruning of hierarchical model based clustering
The goal of clustering is to identify distinct groups in a dataset. The basic idea of model-based clustering is to approximate the data density by a mixture model, typically a mix...
Jeremy Tantrum, Alejandro Murua, Werner Stuetzle