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SIGMOD
1996
ACM
151views Database» more  SIGMOD 1996»
15 years 10 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 8 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
2007
SIAM
122views Data Mining» more  SDM 2007»
15 years 8 months ago
Incremental Spectral Clustering With Application to Monitoring of Evolving Blog Communities
In recent years, spectral clustering method has gained attentions because of its superior performance compared to other traditional clustering algorithms such as K-means algorithm...
Huazhong Ning, Wei Xu, Yun Chi, Yihong Gong, Thoma...
SDM
2003
SIAM
184views Data Mining» more  SDM 2003»
15 years 7 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
BMCBI
2007
237views more  BMCBI 2007»
15 years 6 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