An effective approach based on rough set and topic cluster to build peer communities

Abstract

A peer community is composed of a number of peers who share files about the same topic in the file sharing P2P applications. Building peer communities can benefit content location and retrieval in P2P systems. We propose an effective approach based on rough set and topic cluster to build peer communities. Firstly, we compute one of the best reduced sets of all the same type files, such as the video files, with files' attributes in a peer. Secondly, topic clusters of a peer are calculated, which represent the interests of it. Finally, we build peer communities using the super peer technique. Experiments performed on the real data sets prove that our approach is effective. Experimental results verify that our approach works much better compared with that of previous approaches. © Springer-Verlag Berlin Heidelberg 2007.