  Cluster analysis methods seek to partition a data set into homogeneous
subgroups. It is useful in a wide variety of applications, including document
processing and modern genetics. Conventional clustering methods are
unsupervised, meaning that there is no outcome variable nor is anything known
about the relationship between the observations in the data set. In many
situations, however, information about the clusters is available in addition to
the values of the features. For example, the cluster labels of some
observations may be known, or certain observations may be known to belong to
the same cluster. In other cases, one may wish to identify clusters that are
associated with a particular outcome variable. This review describes several
clustering algorithms (known as "semi-supervised clustering" methods) that can
be applied in these situations. The majority of these methods are modifications
of the popular k-means clustering method, and several of them will be described
in detail. A brief description of some other semi-supervised clustering
algorithms is also provided.
