Semi-supervised clustering methods
It provides a review of methods for researchers and practitioners needing to incorporate prior knowledge into clustering tasks, but it is incremental as it focuses on summarizing existing algorithms.
This review addresses the problem of clustering when partial information about clusters is available, such as known labels or pairwise constraints, by describing various semi-supervised clustering algorithms, primarily modifications of k-means, to handle these scenarios.
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.