  The increasing nature of World Wide Web has imposed great challenges for
researchers in improving the search efficiency over the internet. Now days web
document clustering has become an important research topic to provide most
relevant documents in huge volumes of results returned in response to a simple
query. In this paper, first we proposed a novel approach, to precisely define
clusters based on maximal frequent item set (MFI) by Apriori algorithm.
Afterwards utilizing the same maximal frequent item set (MFI) based similarity
measure for Hierarchical document clustering. By considering maximal frequent
item sets, the dimensionality of document set is decreased. Secondly, providing
privacy preserving of open web documents is to avoiding duplicate documents.
There by we can protect the privacy of individual copy rights of documents.
This can be achieved using equivalence relation.
