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SIGMOD
1998
ACM
233views Database» more  SIGMOD 1998»
9 years 2 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...
SDM
2004
SIAM
253views Data Mining» more  SDM 2004»
8 years 11 months ago
Density-Connected Subspace Clustering for High-Dimensional Data
Several application domains such as molecular biology and geography produce a tremendous amount of data which can no longer be managed without the help of efficient and effective ...
Peer Kröger, Hans-Peter Kriegel, Karin Kailin...
SDM
2009
SIAM
205views Data Mining» more  SDM 2009»
9 years 7 months ago
Identifying Information-Rich Subspace Trends in High-Dimensional Data.
Identifying information-rich subsets in high-dimensional spaces and representing them as order revealing patterns (or trends) is an important and challenging research problem in m...
Chandan K. Reddy, Snehal Pokharkar
CORR
2010
Springer
219views Education» more  CORR 2010»
8 years 10 months ago
Clustering high dimensional data using subspace and projected clustering algorithms
: Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerate...
Rahmat Widia Sembiring, Jasni Mohamad Zain, Abdull...
CIKM
2008
Springer
9 years 5 days ago
REDUS: finding reducible subspaces in high dimensional data
Finding latent patterns in high dimensional data is an important research problem with numerous applications. The most well known approaches for high dimensional data analysis are...
Xiang Zhang, Feng Pan, Wei Wang 0010
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