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Document Clustering with Universum

Dan Zhang
Graduate Student
Computer Science
Purdue University

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Document Clustering with Universum

Keywords:

document clustering, automatic topic extraction, document organization, Clustering, Virtual Brown Bag Research Series,

Abstract

Document clustering is a popular research topic, which aims to partition documents into groups of similar objects (i.e., clusters), and has been widely used in many applications such as automatic topic extraction, document organization and filtering. As a recently proposed concept, Universum is a collection of "non-examples" that do not belong to any concept/cluster of interest. This paper proposes a novel document clustering technique, Document Clustering with Universum, which utilizes the Universum examples to improve the clustering performance.

The intuition is that the Universum examples can serve as supervised information and help improve the performance of clustering, since they are known not belonging to any meaningful concepts/clusters in the target domain. In particular, a maximum margin clustering method is proposed to model both target examples and Universum examples for clustering. An extensive set of experiments is conducted to demonstrate the effectiveness and efficiency of the proposed algorithm.