The article introduces two new clustering methods that use a small amount of labeled data to improve the grouping of unlabeled data. These methods are based on a technique called locality sensitive k-means clustering and are inspired by previous semi-supervised clustering approaches. The researchers tested their methods on both artificial and real datasets and found that they significantly enhance clustering performance compared to other similar algorithms.