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» From Comparing Clusterings to Combining Clusterings
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TKDE
2008
197views more  TKDE 2008»
13 years 7 months ago
Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters
In this paper, we present an agglomerative fuzzy K-Means clustering algorithm for numerical data, an extension to the standard fuzzy K-Means algorithm by introducing a penalty term...
Mark Junjie Li, Michael K. Ng, Yiu-ming Cheung, Jo...
BMCBI
2010
115views more  BMCBI 2010»
13 years 7 months ago
Multiconstrained gene clustering based on generalized projections
Background: Gene clustering for annotating gene functions is one of the fundamental issues in bioinformatics. The best clustering solution is often regularized by multiple constra...
Jia Zeng, Shanfeng Zhu, Alan Wee-Chung Liew, Hong ...
SDM
2009
SIAM
220views Data Mining» more  SDM 2009»
14 years 5 months ago
Bayesian Cluster Ensembles.
Cluster ensembles provide a framework for combining multiple base clusterings of a dataset to generate a stable and robust consensus clustering. There are important variants of th...
Hongjun Wang, Hanhuai Shan, Arindam Banerjee
ACL
2008
13 years 9 months ago
Inducing Gazetteers for Named Entity Recognition by Large-Scale Clustering of Dependency Relations
We propose using large-scale clustering of dependency relations between verbs and multiword nouns (MNs) to construct a gazetteer for named entity recognition (NER). Since dependen...
Jun'ichi Kazama, Kentaro Torisawa
BMCBI
2010
94views more  BMCBI 2010»
13 years 7 months ago
A comparative study of conservation and variation scores
Background: Conservation and variation scores are used when evaluating sites in a multiple sequence alignment, in order to identify residues critical for structure or function. A ...
Fredrik Johansson, Hiroyuki Toh