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» Mining Very Large Databases
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ICDE
2012
IEEE
227views Database» more  ICDE 2012»
12 years 8 days ago
Horizontal Reduction: Instance-Level Dimensionality Reduction for Similarity Search in Large Document Databases
—Dimensionality reduction is essential in text mining since the dimensionality of text documents could easily reach several tens of thousands. Most recent efforts on dimensionali...
Min-Soo Kim 0001, Kyu-Young Whang, Yang-Sae Moon
KDD
2007
ACM
211views Data Mining» more  KDD 2007»
14 years 10 months ago
Enhanced max margin learning on multimodal data mining in a multimedia database
The problem of multimodal data mining in a multimedia database can be addressed as a structured prediction problem where we learn the mapping from an input to the structured and i...
Zhen Guo, Zhongfei Zhang, Eric P. Xing, Christos F...
VLDB
1998
ACM
94views Database» more  VLDB 1998»
14 years 2 months ago
Issues in Developing Very Large Data Warehouses
The size of The Boeing Company posts some stringent requirements on data warehouse design and implementation. We summarize four interesting and challenging issues in developing ve...
Lyman Do, Pamela Drew, Wei Jin, Vish Jumani, David...
ESWA
2006
139views more  ESWA 2006»
13 years 9 months ago
An efficient data mining approach for discovering interesting knowledge from customer transactions
Mining association rules and mining sequential patterns both are to discover customer purchasing behaviors from a transaction database, such that the quality of business decision ...
Show-Jane Yen, Yue-Shi Lee
GFKL
2007
Springer
163views Data Mining» more  GFKL 2007»
14 years 1 months ago
Fast Support Vector Machine Classification of Very Large Datasets
In many classification applications, Support Vector Machines (SVMs) have proven to be highly performing and easy to handle classifiers with very good generalization abilities. Howe...
Janis Fehr, Karina Zapien Arreola, Hans Burkhardt