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» OP-Cluster: Clustering by Tendency in High Dimensional Space
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ICMCS
2006
IEEE
105views Multimedia» more  ICMCS 2006»
14 years 1 months ago
Entropy and Memory Constrained Vector Quantization with Separability Based Feature Selection
An iterative model selection algorithm is proposed. The algorithm seeks relevant features and an optimal number of codewords (or codebook size) as part of the optimization. We use...
Sangho Yoon, Robert M. Gray
ISCC
2006
IEEE
123views Communications» more  ISCC 2006»
14 years 1 months ago
Similarity Search in a Hybrid Overlay P2P Network
P2P systems are increasingly used to discover and share various data between users. The performance of a P2P based information retrieval system is determined by the efficiency of...
Mouna Kacimi, Kokou Yétongnon
ESANN
2006
13 years 8 months ago
Data topology visualization for the Self-Organizing Map
The Self-Organizing map (SOM), a powerful method for data mining and cluster extraction, is very useful for processing data of high dimensionality and complexity. Visualization met...
Kadim Tasdemir, Erzsébet Merényi
BMCBI
2008
148views more  BMCBI 2008»
13 years 7 months ago
Discovering biclusters in gene expression data based on high-dimensional linear geometries
Background: In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classifi...
Xiangchao Gan, Alan Wee-Chung Liew, Hong Yan
BMCBI
2010
243views more  BMCBI 2010»
13 years 7 months ago
Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data
Background: Visualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new ...
Christoph Bartenhagen, Hans-Ulrich Klein, Christia...