Work

Semi‐supervised clustering methods

Year: 2013

Type: article

Abstract: 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.

Source: Wiley Interdisciplinary Reviews Computational Statistics

DOI: 10.1002/wics.1270

Author: Eric Bair

Institution: University of North Carolina at Chapel Hill

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institute of Environmental Health Sciences, National Center for Research Resources, National Center for Advancing Translational Sciences

Awards: UNC Clinical and Translational Science Award, UNC Center for Environmental Health and Susceptibility