Learning heterogeneous coupling relationships between non-iid terms

With the rapid proliferation of social media and online community, a vast amount of text data has been generated. Discovering the insightful value of the text data has increased its importance, a variety of text mining and process algorithms have been created in the recent years such as classification, clustering, similarity comparison. Most previous research uses a vector-space model for text representation and analysis. However, the vector-space model does not utilise the information about the relationships between the term to term. Moreover, the classic classification methods also ignore the relationships between each text document to another. In other word, the traditional text mining techniques assume the relation between terms and between documents are independent and identically distributed (iid). In this paper, we will introduce a novel term representation by involving the coupled relations from term to term. This coupled representation provides much richer information that enables us to create a coupled similarity metric for measuring document similarity, and a coupled document similarity based K-Nearest centroid classifier will be applied to the classification task. Experiments verify the proposed approach outperforming the classic vector-space based classifier, and show potential advantages and richness in exploring the other text mining tasks.