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ICPR
2008
IEEE
13 years 11 months ago
Feature selection for clustering with constraints using Jensen-Shannon divergence
In semi-supervised clustering, domain knowledge can be converted to constraints and used to guide the clustering. In this paper we propose a feature selection algorithm for semi-s...
Yuanhong Li, Ming Dong, Yunqian Ma
SDM
2008
SIAM
136views Data Mining» more  SDM 2008»
13 years 6 months ago
Exploration and Reduction of the Feature Space by Hierarchical Clustering
In this paper we propose and test the use of hierarchical clustering for feature selection. The clustering method is Ward's with a distance measure based on GoodmanKruskal ta...
Dino Ienco, Rosa Meo
BMCBI
2004
181views more  BMCBI 2004»
13 years 4 months ago
Iterative class discovery and feature selection using Minimal Spanning Trees
Background: Clustering is one of the most commonly used methods for discovering hidden structure in microarray gene expression data. Most current methods for clustering samples ar...
Sudhir Varma, Richard Simon
PRL
2010
188views more  PRL 2010»
12 years 11 months ago
A fast divisive clustering algorithm using an improved discrete particle swarm optimizer
As an important technique for data analysis, clustering has been employed in many applications such as image segmentation, document clustering and vector quantization. Divisive cl...
Liang Feng, Ming-Hui Qiu, Yu-Xuan Wang, Qiao-Liang...
IMCSIT
2010
13 years 2 months ago
Evaluation of Clustering Algorithms for Polish Word Sense Disambiguation
Word Sense Disambiguation in text is still a difficult problem as the best supervised methods require laborious and costly manual preparation of training data. Thus, this work focu...
Bartosz Broda, Wojciech Mazur