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EMO
2005
Springer
110views Optimization» more  EMO 2005»
15 years 10 months ago
Parallelization of Multi-objective Evolutionary Algorithms Using Clustering Algorithms
Abstract. While Single-Objective Evolutionary Algorithms (EAs) parallelization schemes are both well established and easy to implement, this is not the case for Multi-Objective Evo...
Felix Streichert, Holger Ulmer, Andreas Zell
CIDM
2009
IEEE
15 years 11 months ago
Density-based clustering of polygons
– Clustering is an important task in spatial data mining and spatial analysis. We propose a clustering algorithm P-DBSCAN to cluster polygons in space. PDBSCAN is based on the we...
Deepti Joshi, Ashok Samal, Leen-Kiat Soh
SDM
2009
SIAM
220views Data Mining» more  SDM 2009»
16 years 1 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
BMCBI
2008
142views more  BMCBI 2008»
15 years 4 months ago
Genetic weighted k-means algorithm for clustering large-scale gene expression data
Background: The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It...
Fang-Xiang Wu
ESORICS
2005
Springer
15 years 10 months ago
Privacy Preserving Clustering
The freedom and transparency of information flow on the Internet has heightened concerns of privacy. Given a set of data items, clustering algorithms group similar items together...
Somesh Jha, Louis Kruger, Patrick McDaniel