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KDD
2009
ACM
229views Data Mining» more  KDD 2009»
14 years 9 months ago
An association analysis approach to biclustering
The discovery of biclusters, which denote groups of items that show coherent values across a subset of all the transactions in a data set, is an important type of analysis perform...
Gaurav Pandey, Gowtham Atluri, Michael Steinbach, ...
KDD
2008
ACM
119views Data Mining» more  KDD 2008»
14 years 9 months ago
SAIL: summation-based incremental learning for information-theoretic clustering
Information-theoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is so-called INFO-K-means, which perfor...
Junjie Wu, Hui Xiong, Jian Chen
KDD
2008
ACM
159views Data Mining» more  KDD 2008»
14 years 9 months ago
Semi-supervised learning with data calibration for long-term time series forecasting
Many time series prediction methods have focused on single step or short term prediction problems due to the inherent difficulty in controlling the propagation of errors from one ...
Haibin Cheng, Pang-Ning Tan
KDD
2004
ACM
150views Data Mining» more  KDD 2004»
14 years 9 months ago
A framework for ontology-driven subspace clustering
Traditional clustering is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. While domain knowledge is always the bes...
Jinze Liu, Wei Wang 0010, Jiong Yang
KDD
2004
ACM
139views Data Mining» more  KDD 2004»
14 years 9 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