  We consider the problem of data clustering with unidentified feature quality
and when a small amount of labelled data is provided. An unsupervised sparse
clustering method can be employed in order to detect the subgroup of features
necessary for clustering and a semi-supervised method can use the labelled data
to create constraints and enhance the clustering solution. In this paper we
propose a K-Means variant that employs these techniques. We show that the
algorithm maintains the high performance of other semi-supervised algorithms
and in addition preserves the ability to identify informative from
uninformative features. We examine the performance of the algorithm on
synthetic and real world data sets. We use scenarios of different number and
types of constraints as well as different clustering initialisation methods.
