Privacy Preserving MFI Based Similarity Measure For Hierarchical Document Clustering Privacy Preserving MFI Based Similarity Measure For Hierarchical Document Clustering      

By  White Globe, On July 14, 2012

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.

hierarchical routing and addressing in the Internet hierarchical routing and addressing in the Internet      

By  Joseff Haldor , On February 5, 2009

A Mirroring Theorem and its Application to a New Method of Unsupervised Hierarchical Pattern Classification A Mirroring Theorem and its Application to a New Method of Unsupervised Hierarchical Pattern Classification      

By  Ijcsis Editor, On November 10, 2009

In this paper, we prove a crucial theorem called “Mirroring Theorem” which affirms that given a collection of samples with enough information in it such that it can be classified into classes and sub-classes then (i) There exists a mapping which classifies and subclassifies these samples (ii) There exists a hierarchical classifier which can be constructed by using Mirroring Neural Networks (MNNs) in combination with a clustering algorithm that can approximate this mapping. Thus, the proof of the Mirroring theorem provides a theoretical basis for the existence and a practical feasibility of constructing hierarchical classifiers, given the maps. Our proposed Mirroring Theorem can also be considered as an extension to Kolmogrov’s theorem in providing a realistic solution for unsupervised classification. The techniques we develop, are general in nature and have led to the construction of learning machines which are (i) tree like in structure, (ii) modular (iii) with each module running on a common algorithm (tandem algorithm) and (iv) self-supervised. We have actually built the architecture, developed the tandem algorithm of such a hierarchical classifier and demonstrated it on an example problem.

11- k-Means Clustering, Hierarchical Clustering 11- k-Means Clustering, Hierarchical Clustering      

By  Bar tender, Massachusetts Institute of Technology, Massachusetts, On August 27, 2008