  Unsupervised clustering algorithms for vectors has been widely used in the
area of machine learning. Many applications, including the biological data we
studied in this paper, contain some boundary datapoints which show combination
properties of two underlying clusters and could lower the performance of the
traditional clustering algorithms. We develop a confident clustering method
aiming to diminish the influence of these datapoints and improve the clustering
results. Concretely, for a list of datapoints, we give two clustering results.
The first-round clustering attempts to classify only pure vectors with high
confidence. Based on it, we classify more vectors with less confidence in the
second round. We validate our algorithm on single-cell RNA-seq data, which is a
powerful and widely used tool in biology area. Our confident clustering shows a
high accuracy on our tested datasets. In addition, unlike traditional
clustering methods in single-cell analysis, the confident clustering shows high
stability under different choices of parameters.
