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KDD
2008
ACM
172views Data Mining» more  KDD 2008»
15 years 10 months ago
Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Jason V. Davis, Inderjit S. Dhillon
KDD
2006
ACM
134views Data Mining» more  KDD 2006»
15 years 10 months ago
Learning to rank networked entities
Several algorithms have been proposed to learn to rank entities modeled as feature vectors, based on relevance feedback. However, these algorithms do not model network connections...
Alekh Agarwal, Soumen Chakrabarti, Sunny Aggarwal
KDD
2005
ACM
218views Data Mining» more  KDD 2005»
15 years 10 months ago
A maximum entropy web recommendation system: combining collaborative and content features
Web users display their preferences implicitly by navigating through a sequence of pages or by providing numeric ratings to some items. Web usage mining techniques are used to ext...
Xin Jin, Yanzan Zhou, Bamshad Mobasher
KDD
2004
ACM
134views Data Mining» more  KDD 2004»
15 years 10 months ago
Exploiting a support-based upper bound of Pearson's correlation coefficient for efficiently identifying strongly correlated pair
Given a user-specified minimum correlation threshold and a market basket database with N items and T transactions, an all-strong-pairs correlation query finds all item pairs with...
Hui Xiong, Shashi Shekhar, Pang-Ning Tan, Vipin Ku...
KDD
2004
ACM
209views Data Mining» more  KDD 2004»
15 years 10 months ago
A data mining approach to modeling relationships among categories in image collection
This paper proposes a data mining approach to modeling relationships among categories in image collection. In our approach, with image feature grouping, a visual dictionary is cre...
Ruofei Zhang, Zhongfei (Mark) Zhang, Sandeep Khanz...