TY - GEN

T1 - PageRank based clustering of hypertext document collections

AU - Avrachenkov, Konstantin

AU - Dobrynin, Vladimir

AU - Nemirovsky, Danil

AU - Pham, Son Kim

AU - Smirnova, Elena

PY - 2008/12/15

Y1 - 2008/12/15

N2 - Clustering hypertext document collection is an important task in Information Retrieval. Most clustering methods are based on document content and do not take into account the hyper-text links. Here we propose a novel PageRank based clustering (PRC) algorithm which uses the hypertext structure. The PRC algorithm produces graph partitioning with high modularity and coverage. The comparison of the PRC algorithm with two content based clustering algorithms shows that there is a good match between PRC clustering and content based clustering.

AB - Clustering hypertext document collection is an important task in Information Retrieval. Most clustering methods are based on document content and do not take into account the hyper-text links. Here we propose a novel PageRank based clustering (PRC) algorithm which uses the hypertext structure. The PRC algorithm produces graph partitioning with high modularity and coverage. The comparison of the PRC algorithm with two content based clustering algorithms shows that there is a good match between PRC clustering and content based clustering.

KW - Directed graphs

KW - PageRank based clustering

UR - http://www.scopus.com/inward/record.url?scp=57349135216&partnerID=8YFLogxK

U2 - 10.1145/1390334.1390549

DO - 10.1145/1390334.1390549

M3 - Conference contribution

AN - SCOPUS:57349135216

SN - 9781605581644

SP - 873

EP - 874

BT - ACM SIGIR 2008 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings

T2 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM SIGIR 2008

Y2 - 20 July 2008 through 24 July 2008

ER -