  The traditional apriori algorithm can be used for clustering the web
documents based on the association technique of data mining. But this algorithm
has several limitations due to repeated database scans and its weak association
rule analysis. In modern world of large databases, efficiency of traditional
apriori algorithm would reduce manifolds. In this paper, we proposed a new
modified apriori approach by cutting down the repeated database scans and
improving association analysis of traditional apriori algorithm to cluster the
web documents. Further we improve those clusters by applying Fuzzy C-Means
(FCM), K-Means and Vector Space Model (VSM) techniques separately. For
experimental purpose, we use Classic3 and Classic4 datasets of Cornell
University having more than 10,000 documents and run both traditional apriori
and our modified apriori approach on it. Experimental results show that our
approach outperforms the traditional apriori algorithm in terms of database
scan and improvement on association of analysis. We found out that FCM is
better than K-Means and VSM in terms of F-measure of clusters of different
sizes.
