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SIGMOD
1996
ACM
151views Database» more  SIGMOD 1996»
15 years 8 months ago
BIRCH: An Efficient Data Clustering Method for Very Large Databases
Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely st,udied problems in this area is the identification of clusters...
Tian Zhang, Raghu Ramakrishnan, Miron Livny
SDM
2008
SIAM
139views Data Mining» more  SDM 2008»
15 years 5 months ago
Simultaneous Unsupervised Learning of Disparate Clusterings
Most clustering algorithms produce a single clustering for a given data set even when the data can be clustered naturally in multiple ways. In this paper, we address the difficult...
Prateek Jain, Raghu Meka, Inderjit S. Dhillon
SDM
2004
SIAM
212views Data Mining» more  SDM 2004»
15 years 5 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...
BMCBI
2007
237views more  BMCBI 2007»
15 years 4 months ago
FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data
Background: Data clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. To this aim, existing cluster...
Limin Fu, Enzo Medico
BMCBI
2007
173views more  BMCBI 2007»
15 years 4 months ago
Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data
Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...