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» Mining for the most certain predictions from dyadic data
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KDD
2010
ACM
222views Data Mining» more  KDD 2010»
13 years 8 months ago
Large linear classification when data cannot fit in memory
Recent advances in linear classification have shown that for applications such as document classification, the training can be extremely efficient. However, most of the existing t...
Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-J...
KDD
2012
ACM
212views Data Mining» more  KDD 2012»
11 years 9 months ago
Harnessing the wisdom of the crowds for accurate web page clipping
Clipping Web pages, namely extracting the informative clips (areas) from Web pages, has many applications, such as Web printing and e-reading on small handheld devices. Although m...
Lei Zhang, Linpeng Tang, Ping Luo, Enhong Chen, Li...
KDD
2005
ACM
107views Data Mining» more  KDD 2005»
14 years 6 days ago
Predicting the product purchase patterns of corporate customers
This paper describes TIPPPS (Time Interleaved Product Purchase Prediction System), which analyses billing data of corporate customers in a large telecommunications company in orde...
Bhavani Raskutti, Alan Herschtal
IADIS
2008
13 years 8 months ago
Customer Insights from Transactional Database: Database Marketing Case
Marketing databases are currently one of the most important resources in any marketing departments. Regarding their customer knowledge needs many of them had develop their own dat...
Filipe Mota Pinto, Alzira Ascensão Marques,...
KDD
2001
ACM
253views Data Mining» more  KDD 2001»
14 years 7 months ago
GESS: a scalable similarity-join algorithm for mining large data sets in high dimensional spaces
The similarity join is an important operation for mining high-dimensional feature spaces. Given two data sets, the similarity join computes all tuples (x, y) that are within a dis...
Jens-Peter Dittrich, Bernhard Seeger