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In this paper we propose a method to improve web search results in search engines. The Self Organizing Map is used for clustering query logs in order to identify prominent groups of user query terms for further analysis. Such groups can provide meaningful information regarding web users' search interests. Identified clusters can further be used for developing an adaptive indexing database for improving conventional search engine efficiency. The proposed hybrid model which combines neural network and indexing for web search applications can provide better data filtering effectiveness and efficiently adapt to the changes based on the web searchers' interests or behaviour patterns.

Improved Web Searching through Neural Network Based Index Generation