Discriminative k-means clustering

Oggie Arandelovic*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)


The k-means algorithm is a partitional clustering method. Over 60 years old, it has been successfully used for a variety of problems. The popularity of k-means is in large part a consequence of its simplicity and efficiency. In this paper we are inspired by these appealing properties of k-means in the development of a clustering algorithm which accepts the notion of 'positively' and 'negatively' labelled data. The goal is to discover the cluster structure of both positive and negative data in a manner which allows for the discrimination between the two sets. The usefulness of this idea is demonstrated practically on the problem of face recognition, where the task of learning the scope of a person's appearance should be done in a manner which allows this face to be differentiated from others.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Publication statusPublished - 2013
Event2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX, United States
Duration: 4 Aug 20139 Aug 2013


Conference2013 International Joint Conference on Neural Networks, IJCNN 2013
Country/TerritoryUnited States
CityDallas, TX


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