CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Cluster-based language models for distributed retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
SETS: search enhanced by topic segmentation
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
An architecture for information retrieval over semi-collaborating Peer-to-Peer networks
Proceedings of the 2004 ACM symposium on Applied computing
Using locality of reference to improve performance of peer-to-peer applications
WOSP '04 Proceedings of the 4th international workshop on Software and performance
An Effective Resource Description Based Approach to Find Similar Peers
P2P '05 Proceedings of the Fifth IEEE International Conference on Peer-to-Peer Computing
An empirical study of free-riding behavior in the maze p2p file-sharing system
IPTPS'05 Proceedings of the 4th international conference on Peer-to-Peer Systems
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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.