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Estimating the number of clusters (K) is a critical and often difficult taskin cluster analysis. Many methods have been proposed to estimate K, includingsome top performers using resampling approach. When performing cluster analysisin high-dimensional data, simultaneous clustering and feature selection isneeded for improved interpretation and performance. To our knowledge, none hasinvestigated simultaneous estimation of K and feature selection in anexploratory cluster analysis. In this paper, we propose a resampling method tomeet this gap and evaluate its performance under the sparse K-means clusteringframework. The proposed target function balances between sensitivity andspecificity of clustering evaluation of pairwise subjects from clustering offull and subsampled data. Through extensive simulations, the method performsamong the best over classical methods in estimating K in low-dimensional data.For high-dimensional simulation data, it also shows superior performance tosimultaneously estimate K and feature sparsity parameter. Finally, we evaluatedthe methods in four microarray, two RNA-seq, one SNP and two non-omicsdatasets. The proposed method achieves better clustering accuracy with fewerselected predictive genes in almost all real applications.

Simultaneous Estimation of Number of Clusters and Feature Sparsity in Clustering High-Dimensional Data