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» An objective approach to cluster validation
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GECCO
2007
Springer
162views Optimization» more  GECCO 2007»
15 years 4 months ago
A multi-objective approach to discover biclusters in microarray data
The main motivation for using a multi–objective evolutionary algorithm for finding biclusters in gene expression data is motivated by the fact that when looking for biclusters ...
Federico Divina, Jesús S. Aguilar-Ruiz
NETWORKING
2010
14 years 8 months ago
Speculative Validation of Web Objects for Further Reducing the User-Perceived Latency
Web caching techniques reduce user-perceived latency by serving the most popular web objects from an intermediate memory. In order to assure that reused objects are not stale, cond...
Josep Domènech, José A. Gil, Julio S...
IDA
2009
Springer
14 years 7 months ago
Context-Based Distance Learning for Categorical Data Clustering
Abstract. Clustering data described by categorical attributes is a challenging task in data mining applications. Unlike numerical attributes, it is difficult to define a distance b...
Dino Ienco, Ruggero G. Pensa, Rosa Meo
FSS
2002
99views more  FSS 2002»
14 years 9 months ago
Extreme physical information and objective function in fuzzy clustering
Fuzzy clustering algorithms have been widely studied and applied in a variety of areas. They become the major techniques7 in cluster analysis. In this paper, we focus on objective...
Michel Ménard, Michel Eboueya
TKDE
2008
197views more  TKDE 2008»
14 years 9 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...