  The dynamic web has increased exponentially over the past few years with more
than thousands of documents related to a subject available to the user now.
Most of the web documents are unstructured and not in an 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 documents at the beginning.. Besides the particular
clustering algorithm, the different term weighting functions applied to the
selected features to represent web document is a main aspect in clustering
task. Keeping this approach in mind, here we proposed a new mechanism called
Tf-Idf based Apriori for clustering the web documents. We then rank the
documents in each cluster using Tf-Idf and similarity factor of documents based
on the user query. This approach will helps the user to get all his relevant
documents in one place and can restrict his search to some top documents of his
choice. For experimental purpose, we have taken the Classic3 and Classic4
datasets of Cornell University having more than 10,000 documents and use gensim
toolkit to carry out our work. We have compared our approach with traditional
apriori algorithm and found that our approach is giving better results for
higher minimum support. Our ranking mechanism is also giving a good F-measure
of 78%.
