Abstract
New methods that are user-friendly and efficient are needed for guidance among the masses of textual information available in the Internet and the World Wide Web. This paper describes text mining has been gaining popularity in the knowledge discovery field, particularity with the increasing availability of digital documents in various languages from all around the world. In this work, we attempt to develop a language-neutral method to tackle the linguistics difficulties in the text mining process. Using a variation of automatic clustering techniques, which apply a neural net approach, namely the Self-Organizing Maps (SOM). The SOM is used to generate two maps, namely the word cluster map and the document cluster map, which reveal the relationships among words and documents respectively. The search process incorporates these two maps and effectively finds the relevant documents according to the keywords specified in the query. The conceptually associated web documents are found not only by the specific keywords but the relevant words found by the word cluster map.
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