Abstract

Three methods for combining multiple clustering systems are presented and evaluated, focusing on the problem of finding the correspondence between clusters of di#erent systems. In this work, the clusters of individual systems are represented in a common space and their correspondence estimated by either "clustering clusters" or with Singular Value Decomposition. The approaches are evaluated for the task of topic discovery on three major corpora and eight di#erent clustering algorithms and it is shown experimentally that combination schemes almost always o#er gains compared to single systems, but gains from using a combination scheme depend on the underlying clustering systems.

Citations