Evolving document features for Web document clustering: a feasibility study

Document analysis and its associated research underpins Web intelligence and the envisaged 'semantic Web'. A key issue is how to encode a document without losing salient information. Current research almost always uses fixed-length vectors based on word (term) frequency (TF) and/or variants thereof. We explore the question of alternative encodings, and we search for such encodings using an evolutionary algorithm (EA). These alternatives consider a variety of other features that can be extracted from a document, and the EA explores the space of weighted combinations of these. Tests on the BankSearch dataset were able to find encodings which outperformed previous results using TF-based encodings. Among several tentative findings it seems clear that the ideal encoding is highly task-dependent, and we can recommend certain features as useful for specific types of document clustering tasks.