Privacy Preserving MFI Based Similarity Measure for Hierarchical Document Clustering

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Author(s):
P. Rajesh, G. Narasimha, N. Saisumanth
Published Date:
July 05, 2012
Issue:
Volume 2, Issue 4
Page(s):
7 - 12
DOI:
10.7815/ijorcs.24.2012.033
Views:
4430
Downloads:
380

Keywords:
maximal frequent item set, apriori algorithm, hierarchical document clustering, equivalence relation
Citation:
P. Rajesh, G. Narasimha, N. Saisumanth, "Privacy Preserving MFI Based Similarity Measure for Hierarchical Document Clustering". International Journal of Research in Computer Science, 2 (4): pp. 7-12, July 2012. doi:10.7815/ijorcs.24.2012.033 Other Formats

Abstract

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.

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