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ICML
2004
IEEE
16 years 2 months ago
K-means clustering via principal component analysis
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
Chris H. Q. Ding, Xiaofeng He
82
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IPM
2006
64views more  IPM 2006»
15 years 1 months ago
Text mining without document context
We consider a challenging clustering task: the clustering of muti-word terms without document co-occurrence information in order to form coherent groups of topics. For this task, ...
Eric SanJuan, Fidelia Ibekwe-Sanjuan
CLEF
2010
Springer
15 years 3 months ago
DAEDALUS at WebPS-3 2010: k-Medoids Clustering Using a Cost Function Minimization
This paper describes the participation of DAEDALUS team at the WebPS-3 Task 1, regarding Web People Search. The focus of our research is to evaluate and compare the computational r...
Sara Lana-Serrano, Julio Villena-Román, Jos...
ACL
2001
15 years 3 months ago
Multi-Class Composite N-gram Language Model for Spoken Language Processing Using Multiple Word Clusters
In this paper, a new language model, the Multi-Class Composite N-gram, is proposed to avoid a data sparseness problem for spoken language in that it is difficult to collect traini...
Hirofumi Yamamoto, Shuntaro Isogai, Yoshinori Sagi...
ICML
2006
IEEE
16 years 2 months ago
Practical solutions to the problem of diagonal dominance in kernel document clustering
In supervised kernel methods, it has been observed that the performance of the SVM classifier is poor in cases where the diagonal entries of the Gram matrix are large relative to ...
Derek Greene, Padraig Cunningham