Hierarchically Distributed Peer-to-Peer Document Clustering and Cluster Summarization Export

@article{citeulike:6079820, abstract = {In distributed data mining, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility and scalability, we propose a Hierarchically-distributed Peer-to-Peer (HP2PC) architecture and clustering algorithm. The architecture is based on a multi-layer overlay network of peer neighborhoods. Supernodes, which act as representatives of neighborhoods, are recursively grouped to form higher level neighborhoods. Within a certain level of the hierarchy, peers cooperate within their respective neighborhoods to perform P2P clustering. Using this model, we can partition the clustering problem in a modular way across neighborhoods, solve each part individually using a distributed K-means variant, then successively combine clusterings up the hierarchy where increasingly more global solutions are computed. In addition, for document clustering applications, we summarize the distributed document clusters using a distributed keyphrase extraction algorithm, thus providing interpretation of the clusters. Results show decent speedup, reaching 165 times faster than centralized clustering for a 250-node simulated network, with comparable clustering quality to the centralized approach. We also provide comparison to the P2P K-means algorithm and show that HP2PC accuracy is better for typical hierarchy heights. Results for distributed cluster summarization match those of their centralized counterparts with up to 88\% accuracy.}, address = {Los Alamitos, CA, USA}, author = {Hammouda, Khaled M. and Kamel, Mohamed S.}, citeulike-article-id = {6079820}, citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.189}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/TKDE.2008.189}, doi = {10.1109/TKDE.2008.189}, issn = {1041-4347}, journal = {IEEE Transactions on Knowledge and Data Engineering}, keywords = {2009, distributed\_computing, p2p, tkde}, number = {5}, pages = {681--698}, posted-at = {2009-11-06 03:22:18}, priority = {2}, publisher = {IEEE Computer Society}, title = {Hierarchically Distributed Peer-to-Peer Document Clustering and Cluster Summarization}, url = {http://dx.doi.org/10.1109/TKDE.2008.189}, volume = {21}, year = {2009} }

TY - JOUR ID - citeulike:6079820 L3 - citeulike-article-id:6079820 N2 - In distributed data mining, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility and scalability, we propose a Hierarchically-distributed Peer-to-Peer (HP2PC) architecture and clustering algorithm. The architecture is based on a multi-layer overlay network of peer neighborhoods. Supernodes, which act as representatives of neighborhoods, are recursively grouped to form higher level neighborhoods. Within a certain level of the hierarchy, peers cooperate within their respective neighborhoods to perform P2P clustering. Using this model, we can partition the clustering problem in a modular way across neighborhoods, solve each part individually using a distributed K-means variant, then successively combine clusterings up the hierarchy where increasingly more global solutions are computed. In addition, for document clustering applications, we summarize the distributed document clusters using a distributed keyphrase extraction algorithm, thus providing interpretation of the clusters. Results show decent speedup, reaching 165 times faster than centralized clustering for a 250-node simulated network, with comparable clustering quality to the centralized approach. We also provide comparison to the P2P K-means algorithm and show that HP2PC accuracy is better for typical hierarchy heights. Results for distributed cluster summarization match those of their centralized counterparts with up to 88% accuracy. IS - 5 JF - IEEE Transactions on Knowledge and Data Engineering SN - 1041-4347 AD - Los Alamitos, CA, USA EP - 698 TI - Hierarchically Distributed Peer-to-Peer Document Clustering and Cluster Summarization PB - IEEE Computer Society VL - 21 SP - 681 KW - 2009 KW - distributed_computing KW - p2p KW - tkde AU - Hammouda, Khaled AU - Kamel, Mohamed PY - 2009/// UR - http://dx.doi.org/10.1109/TKDE.2008.189 ER -