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KDD
2004
ACM
624views Data Mining» more  KDD 2004»
14 years 22 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
KDD
2003
ACM
162views Data Mining» more  KDD 2003»
14 years 7 months ago
Improving spatial locality of programs via data mining
In most computer systems, page fault rate is currently minimized by generic page replacement algorithms which try to model the temporal locality inherent in programs. In this pape...
Karlton Sequeira, Mohammed Javeed Zaki, Boleslaw K...
GIS
2002
ACM
13 years 7 months ago
Opening the black box: interactive hierarchical clustering for multivariate spatial patterns
Clustering is one of the most important tasks for geographic knowledge discovery. However, existing clustering methods have two severe drawbacks for this purpose. First, spatial c...
Diansheng Guo, Donna Peuquet, Mark Gahegan
SIGMOD
1998
ACM
233views Database» more  SIGMOD 1998»
13 years 11 months ago
Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications
Data mining applications place special requirements on clustering algorithms including: the ability to nd clusters embedded in subspaces of high dimensional data, scalability, end...
Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopul...
SSD
2007
Springer
131views Database» more  SSD 2007»
14 years 1 months ago
Efficiently Mining Regional Outliers in Spatial Data
With the increasing availability of spatial data in many applications, spatial clustering and outlier detection has received a lot of attention in the database and data mining comm...
Richard Frank, Wen Jin, Martin Ester