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ICCV
2003
IEEE
16 years 8 months ago
Mean Shift Based Clustering in High Dimensions: A Texture Classification Example
Feature space analysis is the main module in many computer vision tasks. The most popular technique, k-means clustering, however, has two inherent limitations: the clusters are co...
Bogdan Georgescu, Ilan Shimshoni, Peter Meer
PAKDD
2009
ACM
186views Data Mining» more  PAKDD 2009»
16 years 1 months ago
Pairwise Constrained Clustering for Sparse and High Dimensional Feature Spaces
Abstract. Clustering high dimensional data with sparse features is challenging because pairwise distances between data items are not informative in high dimensional space. To addre...
Su Yan, Hai Wang, Dongwon Lee, C. Lee Giles
IDA
1999
Springer
15 years 11 months ago
3D Grand Tour for Multidimensional Data and Clusters
Grand tour is a method for viewing multidimensional data via linear projections onto a sequence of two dimensional subspaces and then moving continuously from one projection to the...
Li Yang
DAWAK
2006
Springer
15 years 10 months ago
Achieving k-Anonymity by Clustering in Attribute Hierarchical Structures
Abstract. Individual privacy will be at risk if a published data set is not properly de-identified. k-anonymity is a major technique to de-identify a data set. A more general view ...
Jiuyong Li, Raymond Chi-Wing Wong, Ada Wai-Chee Fu...
BIBE
2001
IEEE
188views Bioinformatics» more  BIBE 2001»
15 years 10 months ago
Interrelated Two-way Clustering: An Unsupervised Approach for Gene Expression Data Analysis
DNA arrays can be used to measure the expression levels of thousands of genes simultaneously. Currently most research focuses on the interpretation of the meaning of the data. How...
Chun Tang, Li Zhang, Aidong Zhang, Murali Ramanath...