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BMVC
2002
15 years 8 months ago
Alignment using Spectral Clusters
This paper describes a hierarchical spectral method for the correspondence matching of point-sets. Conventional spectral methods for correspondence matching are notoriously suscep...
Marco Carcassoni, Edwin R. Hancock
UM
2009
Springer
16 years 29 days ago
Adaptive Clustering of Search Results
Clustering of search results has been shown to be advantageous over the simple list presentation of search results. However, in most clustering interfaces, the clusters are not ada...
Xuehua Shen, ChengXiang Zhai, Nicholas J. Belkin
BMCBI
2008
122views more  BMCBI 2008»
15 years 6 months ago
A practical comparison of two K-Means clustering algorithms
Background: Data clustering is a powerful technique for identifying data with similar characteristics, such as genes with similar expression patterns. However, not all implementat...
Gregory A. Wilkin, Xiuzhen Huang
SIGMOD
2001
ACM
200views Database» more  SIGMOD 2001»
16 years 6 months ago
Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering
In this paper, we investigate how to scale hierarchical clustering methods (such as OPTICS) to extremely large databases by utilizing data compression methods (such as BIRCH or ra...
Markus M. Breunig, Hans-Peter Kriegel, Peer Kr&oum...
BMCBI
2008
142views more  BMCBI 2008»
15 years 6 months ago
Genetic weighted k-means algorithm for clustering large-scale gene expression data
Background: The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It...
Fang-Xiang Wu