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» An efficient clustering method for k-anonymization
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SDM
2004
SIAM
212views Data Mining» more  SDM 2004»
13 years 10 months ago
Clustering with Bregman Divergences
A wide variety of distortion functions, such as squared Euclidean distance, Mahalanobis distance, Itakura-Saito distance and relative entropy, have been used for clustering. In th...
Arindam Banerjee, Srujana Merugu, Inderjit S. Dhil...
SDM
2003
SIAM
184views Data Mining» more  SDM 2003»
13 years 10 months ago
Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
The problem of finding clusters in data is challenging when clusters are of widely differing sizes, densities and shapes, and when the data contains large amounts of noise and out...
Levent Ertöz, Michael Steinbach, Vipin Kumar
PKDD
2000
Springer
144views Data Mining» more  PKDD 2000»
14 years 9 days ago
Fast Hierarchical Clustering Based on Compressed Data and OPTICS
: One way to scale up clustering algorithms is to squash the data by some intelligent compression technique and cluster only the compressed data records. Such compressed data recor...
Markus M. Breunig, Hans-Peter Kriegel, Jörg S...
ICCV
2009
IEEE
1821views Computer Vision» more  ICCV 2009»
15 years 1 months ago
Feature Correspondence and Deformable Object Matching via Agglomerative Correspondence Clustering
We present an efficient method for feature correspondence and object-based image matching, which exploits both photometric similarity and pairwise geometric consistency from local ...
Minsu Cho (Seoul National University), Jungmin Lee...
ISBI
2006
IEEE
14 years 9 months ago
Clustering gene expression patterns of fly embryos
The spatio-temporal patterning of gene expression in early embryos is an important source of information for understanding the functions of genes involved in development. Most ana...
Hanchuan Peng, Fuhui Long, Michael B. Eisen, Eugen...