  The size of web has increased exponentially over the past few years with
thousands of documents related to a subject available to the user. With this
much amount of information available, it is not possible to take the full
advantage of the World Wide Web without having a proper framework to search
through the available data. This requisite organization can be done in many
ways. In this paper we introduce a combine approach to cluster the web pages
which first finds the frequent sets and then clusters the documents. These
frequent sets are generated by using Frequent Pattern growth technique. Then by
applying Fuzzy C- Means algorithm on it, we found clusters having documents
which are highly related and have similar features. We used Gensim package to
implement our approach because of its simplicity and robust nature. We have
compared our results with the combine approach of (Frequent Pattern growth,
K-means) and (Frequent Pattern growth, Cosine_Similarity). Experimental results
show that our approach is more efficient then the above two combine approach
and can handles more efficiently the serious limitation of traditional Fuzzy
C-Means algorithm, which is sensitiveto initial centroid and the number of
clusters to be formed.
