Jana Schmidt, Elisabeth M Brändle, and Stefan Kramer
Clustering with Attribute-Level Constraints
In: 11th IEEE International Conference on Data Mining (ICDM), ed. by Diane J. Cook and Jian Pei and Wei Wang and Osmar R. Zaiane and Xindong Wu, pp. 1206-1211, IEEE.
In many clustering applications the incorporation of background knowledge in the form of
constraints is desirable. In this paper, we introduce a new constraint type and the
corresponding clustering problem: attribute constrained clustering.
The goal is to induce clusters
of binary instances that satisfy constraints on the attribute level. These constraints
specify whether instances may or may not be grouped to a cluster, depending on specific
attribute values. We show how the well-established instance-level constraints, must-link
and cannot-link, can be adapted to the attribute level. A variant of the k-Medoids algorithm
taking into account attribute-level constraints is evaluated on synthetic and real-world data.
Experimental results show that such constraints may provide better clustering
results at lower specification costs if constraints can be expressed on the attribute level.