  Search engine returns thousands of web pages for a single user query, in
which most of them are not relevant. In this context, effective information
retrieval from the expanding web is a challenging task, in particular, if the
query is ambiguous. The major question arises here is that how to get the
relevant pages for an ambiguous query. We propose an approach for the effective
result of an ambiguous query by forming community vector based on association
concept of data minning using vector space model and the freedictionary. We
develop clusters by computing the similarity between community vectors and
document vectors formed from the extracted web pages by the search engine. We
use Gensim package to implement the algorithm because of its simplicity and
robust nature. Analysis shows that our approach is an effective way to form
clusters for an ambiguous query.
