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137
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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
134
Voted
SDM
2007
SIAM
122views Data Mining» more  SDM 2007»
15 years 5 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...
154
Voted
SDM
2003
SIAM
184views Data Mining» more  SDM 2003»
15 years 5 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
151
Voted
ICDM
2010
IEEE
230views Data Mining» more  ICDM 2010»
15 years 1 months ago
Clustering Large Attributed Graphs: An Efficient Incremental Approach
In recent years, many networks have become available for analysis, including social networks, sensor networks, biological networks, etc. Graph clustering has shown its effectivenes...
Yang Zhou, Hong Cheng, Jeffrey Xu Yu
140
Voted
SADM
2010
141views more  SADM 2010»
14 years 10 months ago
A parametric mixture model for clustering multivariate binary data
: The traditional latent class analysis (LCA) uses a mixture model with binary responses on each subject that are independent conditional on cluster membership. However, in many pr...
Ajit C. Tamhane, Dingxi Qiu, Bruce E. Ankenman