  Estimating the number of clusters (K) is a critical and often difficult task
in cluster analysis. Many methods have been proposed to estimate K, including
some top performers using resampling approach. When performing cluster analysis
in high-dimensional data, simultaneous clustering and feature selection is
needed for improved interpretation and performance. To our knowledge, none has
investigated simultaneous estimation of K and feature selection in an
exploratory cluster analysis. In this paper, we propose a resampling method to
meet this gap and evaluate its performance under the sparse K-means clustering
framework. The proposed target function balances between sensitivity and
specificity of clustering evaluation of pairwise subjects from clustering of
full and subsampled data. Through extensive simulations, the method performs
among the best over classical methods in estimating K in low-dimensional data.
For high-dimensional simulation data, it also shows superior performance to
simultaneously estimate K and feature sparsity parameter. Finally, we evaluated
the methods in four microarray, two RNA-seq, one SNP and two non-omics
datasets. The proposed method achieves better clustering accuracy with fewer
selected predictive genes in almost all real applications.
