Integrating Instance-level and Attribute-level Knowledge into Document Clustering
In this paper, we present a document clustering framework incorporating instance-level knowledge in the form of pairwise constraints and attribute-level knowledge in the form of keyphrases. Firstly, we initialize weights based on metric learning with pairwise constraints, then simultaneously learn two kinds of knowledge by combining the distance-based and the constraint-based approaches, finally evaluate and select clustering result based on the degree of users' satisfaction. The experimental results demonstrate the effectiveness and potential of the proposed method.
document clustering pairwise constraints keyphrases