Privacy Preserving MFI Based Similarity Measure For Hierarchical Document Clustering
This addresses the challenge of efficiently clustering web documents for better search results and privacy protection, though it appears incremental in combining existing techniques like MFI and equivalence relations.
The paper tackles the problem of improving search efficiency by proposing a novel approach for hierarchical document clustering using maximal frequent item sets (MFI) to reduce dimensionality and enhance precision, while also incorporating privacy preservation to avoid duplicate documents and protect copyrights.
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.