Clustering stability-based Evolutionary K-Means

Interpolation based consensus clustering for gene expression time series

(2015) Tai-Yu Chiu et al.   BMC BIOINFORMATICS

Recovering the number of clusters in data sets with noise features using feature rescaling factors

(2015) Renato Cordeiro de Amorim et al.   INFORMATION SCIENCES

NbClust: AnRPackage for Determining the Relevant Number of Clusters in a Data Set

(2015) Malika Charrad et al.   Journal of Statistical Software

Limits to robustness and reproducibility in the demarcation of operational taxonomic units

(2014) Thomas S. B. Schmidt et al.   ENVIRONMENTAL MICROBIOLOGY

Evolutionary K-Means with pair-wise constraints

(2014) Zhenfeng He   SOFT COMPUTING

Critical limitations of consensus clustering in class discovery

(2014) Yasin Șenbabaoğlu et al.   Scientific Reports

An extensive comparative study of cluster validity indices

(2012) Olatz Arbelaitz et al.   PATTERN RECOGNITION

A bad instance for k -means++

(2012) Tobias Brunsch et al.   THEORETICAL COMPUTER SCIENCE

How the initialization affects the stability of the қ-means algorithm

(2012) Sébastien Bubeck et al.   ESAIM-Probability and Statistics

Selection of the number of clusters via the bootstrap method

(2011) Yixin Fang et al.   COMPUTATIONAL STATISTICS & DATA ANALYSIS

A new algorithm for initial cluster centers in k-means algorithm

(2011) Murat Erisoglu et al.   PATTERN RECOGNITION LETTERS

Efficiency issues of evolutionary k-means

(2010) M.C. Naldi et al.   APPLIED SOFT COMPUTING

Stability and model selection in k-means clustering

(2010) Ohad Shamir et al.   MACHINE LEARNING

A Survey of Evolutionary Algorithms for Clustering

(2009) E.R. Hruschka et al.   IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND RE

Data clustering: 50 years beyond K-means

(2009) Anil K. Jain   PATTERN RECOGNITION LETTERS