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109
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ICCV
2007
IEEE
16 years 2 months ago
Noise Robust Spectral Clustering
This paper aims to introduce the robustness against noise into the spectral clustering algorithm. First, we propose a warping model to map the data into a new space on the basis o...
Zhenguo Li, Jianzhuang Liu, Shifeng Chen, Xiaoou T...
115
Voted
ICML
2006
IEEE
16 years 1 months ago
Discriminative cluster analysis
Clustering is one of the most widely used statistical tools for data analysis. Among all existing clustering techniques, k-means is a very popular method because of its ease of pr...
Fernando De la Torre, Takeo Kanade
BIBE
2004
IEEE
107views Bioinformatics» more  BIBE 2004»
15 years 4 months ago
Enhanced pClustering and Its Applications to Gene Expression Data
Clustering has been one of the most popular methods to discover useful biological insights from DNA microarray. An interesting paradigm is simultaneous clustering of both genes an...
Sungroh Yoon, Christine Nardini, Luca Benini, Giov...
94
Voted
CSB
2004
IEEE
136views Bioinformatics» more  CSB 2004»
15 years 4 months ago
Minimum Entropy Clustering and Applications to Gene Expression Analysis
Clustering is a common methodology for analyzing the gene expression data. In this paper, we present a new clustering algorithm from an information-theoretic point of view. First,...
Haifeng Li, Keshu Zhang, Tao Jiang
95
Voted
SDM
2003
SIAM
134views Data Mining» more  SDM 2003»
15 years 1 months ago
Hierarchical Document Clustering using Frequent Itemsets
A major challenge in document clustering is the extremely high dimensionality. For example, the vocabulary for a document set can easily be thousands of words. On the other hand, ...
Benjamin C. M. Fung, Ke Wang, Martin Ester