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TKDE
2008
111views more  TKDE 2008»
15 years 6 months ago
Text Clustering with Feature Selection by Using Statistical Data
Abstract-- Feature selection is an important method for improving the efficiency and accuracy of text categorization algorithms by removing redundant and irrelevant terms from the ...
Yanjun Li, Congnan Luo, Soon M. Chung
ICPR
2010
IEEE
15 years 4 months ago
User Adaptive Clustering for Large Image Databases
Abstract--Searching large image databases is a time consuming process when done manually. Current CBIR methods mostly rely on training data in specific domains. When source and dom...
Mohammad Mehdi Saboorian, Mansour Jamzad, Hamid R....
ICML
2004
IEEE
16 years 7 months ago
Solving cluster ensemble problems by bipartite graph partitioning
A critical problem in cluster ensemble research is how to combine multiple clusterings to yield a final superior clustering result. Leveraging advanced graph partitioning techniqu...
Xiaoli Zhang Fern, Carla E. Brodley
KDD
2004
ACM
190views Data Mining» more  KDD 2004»
16 years 7 months ago
Kernel k-means: spectral clustering and normalized cuts
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have re...
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis
PODS
2005
ACM
115views Database» more  PODS 2005»
16 years 6 months ago
A divide-and-merge methodology for clustering
We present a divide-and-merge methodology for clustering a set of objects that combines a top-down "divide" phase with a bottom-up "merge" phase. In contrast, ...
David Cheng, Santosh Vempala, Ravi Kannan, Grant W...