Unsupervised clustering algorithms are a very important source of information for how a dataset may be classified into subgroups of homogeneous sets. For these algorithms we present a full analysis of their quality by introducing a clustering confidentness metric. Based on this metric, a statistical test and a correction of cluster probabilities are introduced to improve the performance of the different algorithms. These results are illustrated by simulation analysis and by an application on a real world data set.