Abstract:
The dynamic web has increased
exponentially over the past few years with more
than thousands of documents related to a subject
available to user now. Most of the web documents
are unstructured and not in organized manner and
hence user facing more difficult to find relevant
documents. A more useful and efficient mechanism
is combining clustering with ranking, where
clustering can group the similar documents in one
place and ranking can be applied to each cluster
for viewing the top document at the beginning.
This paper is proposed tf-idf based MLTransTrie
(Multiple level Association Rule, Transposed
Database,Trie) algorithm for clustering the web
document. We then ranked the documents in each
cluster using tf-idf and similarity factor of
documents based on the user query. This approach
will help the user to get all his relevant document
in one place.
