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» Evaluating Significance of Inconsistencies
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KDD
2008
ACM
161views Data Mining» more  KDD 2008»
16 years 6 months ago
Spectral domain-transfer learning
Traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain. In many real world ap...
Xiao Ling, Wenyuan Dai, Gui-Rong Xue, Qiang Yang, ...
KDD
2007
ACM
177views Data Mining» more  KDD 2007»
16 years 6 months ago
On-board analysis of uncalibrated data for a spacecraft at mars
Analyzing data on-board a spacecraft as it is collected enables several advanced spacecraft capabilities, such as prioritizing observations to make the best use of limited bandwid...
Benyang Tang, Kiri Wagstaff, Rebecca Castañ...
KDD
2007
ACM
159views Data Mining» more  KDD 2007»
16 years 6 months ago
Local decomposition for rare class analysis
Given its importance, the problem of predicting rare classes in large-scale multi-labeled data sets has attracted great attentions in the literature. However, the rare-class probl...
Junjie Wu, Hui Xiong, Peng Wu, Jian Chen
KDD
2007
ACM
182views Data Mining» more  KDD 2007»
16 years 6 months ago
Cleaning disguised missing data: a heuristic approach
In some applications such as filling in a customer information form on the web, some missing values may not be explicitly represented as such, but instead appear as potentially va...
Ming Hua, Jian Pei
KDD
2006
ACM
145views Data Mining» more  KDD 2006»
16 years 6 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...