View-based text representation

Document clustering is useful for many research areas such as Text Mining and Information Retrieval. Therefore, it is desirable to be able to cluster documents accurately. The clustering quality depends not only on the clustering algorithm used but also on the way text is represented in the algorithm. Text is typically represented using the All-Words Vector Space Model in text mining applications. However, this representation's effectiveness is limited when clustering by a specific criterion or view, such as geographical location, and also tends to be computationally expensive due to not reducing the feature space. This paper presents a new representation, the View-Based Vector Space Model, which models a document differently depending on a given view, and is computationally efficient. The basic representation requires predefined ontologies to be available. In order to improve the usefulness of this model, a new text representation learning method which uses Particle Swarm Optimisation to construct ontologies automatically given a set of documents is also presented.