Web is a valuable source of information and it is expanding at an enormous speed. Search engines provide the interface to access to this vast pool of information. Users express their information need through the input query to retrieve the relevant information which does not prove to be effective as input query entered by the user is too short to get the information need of the user. To retrieve the information according to a particular information need from a big pool of information available on the web is a big challenge. This paper proposes a method to find the related queries which approximate the information need of the input query issued to the search engine. This is accomplished using Information scent and content of clicked pages in query sessions mining. Information scent is derived from the information foraging theory in which user behavior in the information environment is guided by information scent. Information need of the query sessions is modeled using information scent and content of clicked URLs and query sessions with similar information need are clustered. The clusters which closely approximate the information need of input query are used to suggest queries with similar information need for a given input query. The suggested queries are ranked in order of their degree of relevance with respect to information need of the input query. Retrieval precision of search engine is improved as suggested queries help to retrieve document relevant to the need of user efficiently and quickly. Experimental study has been conducted on the dataset collected from "Google" search engine Web history to confirm the improvement of precision of information retrieval.