Clustering with Instance-level Constraints

Clustering algorithms conduct a search through the space of possible organizations of a data set. In this paper, we propose two types of instance-level clustering constraints ? must-link and cannot-link constraints ? and show how they can be incorporated into a clustering algorithm to aid that search. For three of the four data sets tested, our results indicate that the incorporation of surprisingly few such constraints can increase clustering accuracy while decreasing runtime. We also investigate the relative effects of each type of constraint and find that the type that contributes most to accuracy improvements depends on the behavior of the clustering algorithm without constraints.

• Type: Conference
• Year: 2000
• Where: ICML
• Authors: Kiri Wagstaff, Claire Cardie
• Keywords: Cannot-link Constraints, Clustering Algorithm, ICML 2000, Instance-level Clustering Constraints, Machine Learning