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Tech Report kmi-00-14 Abstract


Feature Reduction for Document Clustering and Classification
Techreport ID: kmi-00-14
Date: 2000
Author(s): Stefan Rüger and Susan Gauch
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Often users receive search results which contain a wide range of documents, only some of which are relevant to their information needs. To address this problem, ever more systems not only locate information for users, but also organise that information on their behalf. We look at two main automatic approaches to information organisation: interactive clustering of search results and pre-categorising documents to provide hierarchical browsing structures. To be feasible in real world applications, both of these approaches require accurate yet efficient algorithms. Yet, both suffer from the curse of dimensionality - documents are typically represented by hundreds or thousands of words (features) which must be analysed and processed during clustering or classification. In this paper, we discuss feature reduction techniques and their application to document clustering and classification, showing that feature reduction improves efficiency as well as accuracy. We validate these algorithms using human relevance assignments and categorisation.

Publication(s):

DTR 2000/8, Department of Computing, Imperial College London
 
KMi Publications
 

Narrative Hypermedia is...


Narrative Hypermedia
Narrative is concerned fundamentally with coherence, for instance, whether that be a fiction, an historical account or an argument, none of which 'make sense' unless they are put together in a coherent manner.

Hypermedia is the combination of hypertext for linking and structuring multimedia information.

Narrative Hypermedia is therefore concerned with how all of the above narrative forms, plus the many other diverse forms of discourse possible on the Web, can be effectively designed to communicate coherent conceptual structures, drawing inspiration from theories in narratology, semiotics, psycholinguistics and film.