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» An objective evaluation criterion for clustering
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SDM
2009
SIAM
114views Data Mining» more  SDM 2009»
14 years 4 months ago
On the Comparison of Relative Clustering Validity Criteria.
Many different relative clustering validity criteria exist that are very useful in practice as quantitative measures for evaluating the quality of data partitions, and new criter...
Lucas Vendramin, Ricardo J. G. B. Campello, Eduard...
ISBRA
2007
Springer
14 years 1 months ago
Clustering Algorithms Optimizer: A Framework for Large Datasets
Clustering algorithms are employed in many bioinformatics tasks, including categorization of protein sequences and analysis of gene-expression data. Although these algorithms are r...
Roy Varshavsky, David Horn, Michal Linial
IPPS
1998
IEEE
13 years 11 months ago
Comparing the Optimal Performance of Different MIMD Multiprocessor Architectures
We compare the performance of systems consisting of one large cluster containing q processors with systems where processors are grouped into k clusters containing u processors eac...
Lars Lundberg, Håkan Lennerstad
ADMA
2009
Springer
145views Data Mining» more  ADMA 2009»
14 years 2 months ago
A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining
The goal of multi-objective clustering (MOC) is to decompose a dataset into similar groups maximizing multiple objectives in parallel. In this paper, we provide a methodology, arch...
Rachsuda Jiamthapthaksin, Christoph F. Eick, Ricar...
ECML
2006
Springer
13 years 11 months ago
An Adaptive Kernel Method for Semi-supervised Clustering
Semi-supervised clustering uses the limited background knowledge to aid unsupervised clustering algorithms. Recently, a kernel method for semi-supervised clustering has been introd...
Bojun Yan, Carlotta Domeniconi