Abstract

Many real-world datasets can be clustered along multiple dimensions. For example, text documents can be clustered not only by topic, but also by the author’s gender or sentiment. Unfortunately, traditional clustering algorithms produce only a single clustering of a dataset, effectively providing a user with just a single view of the data. In this paper, we propose a new clustering algorithm that can discover in an unsupervised manner each clustering dimension along which a dataset can be meaningfully clustered. Its ability to reveal the important clustering dimensions of a dataset in an unsupervised manner is particularly appealing for those users who have no idea of how a dataset can possibly be clustered. We demonstrate its viability on several challenging text classification tasks.