Clustering Documents with Active Learning Using Wikipedia

Wikipedia has been applied as a background knowledge base to various text mining problems, but very few attempts have been made to utilize it for document clustering. In this paper we propose to exploit the semantic knowledge in Wikipedia for clustering, enabling the automatic grouping of documents with similar themes. Although clustering is intrinsically unsupervised, recent research has shown that incorporating supervision improves clustering performance, even when limited supervision is provided. The approach presented in this paper applies supervision using active learning. We first utilize Wikipedia to create a concept-based representation of a text document, with each concept associated to a Wikipedia article. We then exploit the semantic relatedness between Wikipedia concepts to find pair-wise instance-level constraints for supervised clustering, guiding clustering towards the direction indicated by the constraints. We test our approach on three standard text document dataset...

• Type: Conference
• Year: 2008
• Where: ICDM
• Authors: Anna Huang, David N. Milne, Eibe Frank, Ian H. Witten
• Keywords: Data Mining, Document Clustering, ICDM 2008, Supervised Clustering, Wikipedia