Issue No. 10 - Oct. (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.120
Odysseas Papapetrou , Technical University of Crete, Chania
Wolf Siberski , L3S Research Center, Hannover
Norbert Fuhr , University of Duisburg-Essen, Duisburg
Text clustering is an established technique for improving quality in information retrieval, for both centralized and distributed environments. However, traditional text clustering algorithms fail to scale on highly distributed environments, such as peer-to-peer networks. Our algorithm for peer-to-peer clustering achieves high scalability by using a probabilistic approach for assigning documents to clusters. It enables a peer to compare each of its documents only with very few selected clusters, without significant loss of clustering quality. The algorithm offers probabilistic guarantees for the correctness of each document assignment to a cluster. Extensive experimental evaluation with up to 1 million peers and 1 million documents demonstrates the scalability and effectiveness of the algorithm.
Clustering algorithms, Peer to peer computing, Probabilistic logic, Frequency estimation, Indexing, Computational modeling, text clustering., Distributed clustering
W. Siberski, O. Papapetrou and N. Fuhr, "Decentralized Probabilistic Text Clustering," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 1848-1861, 2012.