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BMCBI
2006
186views more  BMCBI 2006»
15 years 5 months ago
Systematic gene function prediction from gene expression data by using a fuzzy nearest-cluster method
Background: Quantitative simultaneous monitoring of the expression levels of thousands of genes under various experimental conditions is now possible using microarray experiments....
Xiaoli Li, Yin-Chet Tan, See-Kiong Ng
PVLDB
2008
182views more  PVLDB 2008»
15 years 4 months ago
SCOPE: easy and efficient parallel processing of massive data sets
Companies providing cloud-scale services have an increasing need to store and analyze massive data sets such as search logs and click streams. For cost and performance reasons, pr...
Ronnie Chaiken, Bob Jenkins, Per-Åke Larson,...
125
Voted
WILF
2009
Springer
138views Fuzzy Logic» more  WILF 2009»
15 years 11 months ago
An Experimental Validation of Some Indexes of Fuzzy Clustering Similarity
Measuring the similarity between clusterings is a classic problem with several proposed solutions. In this work we focus on measures based on coassociation of data pairs and perfor...
Stefano Rovetta, Francesco Masulli
ICPPW
2003
IEEE
15 years 10 months ago
Parallelization of Cellular Neural Networks for Image Processing on Cluster Architectures
In this paper a simple but effective approach for parallelization of cellular neural networks for image processing is developed. Digital gray-scale images were used to evaluate th...
Thomas Weishäupl, Erich Schikuta
151
Voted
JMLR
2010
225views more  JMLR 2010»
14 years 11 months ago
Hartigan's Method: k-means Clustering without Voronoi
Hartigan's method for k-means clustering is the following greedy heuristic: select a point, and optimally reassign it. This paper develops two other formulations of the heuri...
Matus Telgarsky, Andrea Vattani