An encoding technique based on word importance for the clustering of Web documents

We present a word encoding and clustering technique that groups Web documents based on the importance of the words that appear in the documents. We use a two level self-organizing map architecture to generate clusters of words and documents. We propose that by capturing word importance information of words, similar documents can be then clustered to assist in Web document retrieval. A Web document retrieval system is presented to demonstrate how this approach could. be integrated into Web search.