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KDD
2004
ACM
624views Data Mining» more  KDD 2004»
16 years 6 days ago
Programming the K-means clustering algorithm in SQL
Using SQL has not been considered an efficient and feasible way to implement data mining algorithms. Although this is true for many data mining, machine learning and statistical a...
Carlos Ordonez
CINQ
2004
Springer
180views Database» more  CINQ 2004»
15 years 10 months ago
Interactivity, Scalability and Resource Control for Efficient KDD Support in DBMS
The conflict between resource consumption and query performance in the data mining context often has no satisfactory solution. This not only stands in sharp contrast to the need of...
Matthias Gimbel, Michael Klein, Peter C. Lockemann
IDEAS
2007
IEEE
128views Database» more  IDEAS 2007»
16 years 1 months ago
Streaming Random Forests
Many recent applications deal with data streams, conceptually endless sequences of data records, often arriving at high flow rates. Standard data-mining techniques typically assu...
Hanady Abdulsalam, David B. Skillicorn, Patrick Ma...
SDM
2012
SIAM
224views Data Mining» more  SDM 2012»
13 years 9 months ago
Nearest-Neighbor Search on a Time Budget via Max-Margin Trees
Many high-profile applications pose high-dimensional nearest-neighbor search problems. Yet, it still remains difficult to achieve fast query times for state-of-the-art approache...
Parikshit Ram, Dongryeol Lee, Alexander G. Gray
CIKM
2009
Springer
16 years 1 months ago
A case study on model driven data integration for data centric software development
Model Driven Data Integration is a data integration approach that proactively incorporates and utilizes metadata across the data integration process. By decoupling data and metada...
Hyeonsook Kim, Ying Zhang, Samia Oussena, Tony Cla...