Graph Clustering and Minimum Cut Trees Export

@article{citeulike:3923455, abstract = {In this paper, we introduce simple graph clustering methods based on minimum cuts within the graph. The clustering methods are general enough to apply to any kind of graph but are well suited for graphs where the link structure implies a notion of reference, similarity, or endorsement, such as web and citation graphs. We show that the quality of the produced clusters is bounded by strong minimum cut and expansion criteria. We also develop a framework for hierarchical clustering and present applications to real-world data. We conclude that the clustering algorithms satisfy strong theoretical criteria and perform well in practice.}, author = {Flake, Gary W. and Tarjan, Robert E. and Tsioutsiouliklis, Kostas}, citeulike-article-id = {3923455}, citeulike-linkout-0 = {http://209.85.129.132/search?q=cache:nfa1pV010-gJ:www.internetmathematics.org/volumes/1/4/Flake.pdf+Graph+Clustering+and+Minimum+Cut+Trees&hl=en&ct=clnk&cd=1&client=iceweasel-a}, journal = {Internet Mathematics}, keywords = {categories, dmoz, human, learning}, number = {4}, pages = {385--408}, posted-at = {2009-01-22 11:04:40}, priority = {2}, title = {Graph Clustering and Minimum Cut Trees}, url = {http://209.85.129.132/search?q=cache:nfa1pV010-gJ:www.internetmathematics.org/volumes/1/4/Flake.pdf+Graph+Clustering+and+Minimum+Cut+Trees&hl=en&ct=clnk&cd=1&client=iceweasel-a}, volume = {1} }

TY - JOUR ID - citeulike:3923455 L3 - citeulike-article-id:3923455 N2 - In this paper, we introduce simple graph clustering methods based on minimum cuts within the graph. The clustering methods are general enough to apply to any kind of graph but are well suited for graphs where the link structure implies a notion of reference, similarity, or endorsement, such as web and citation graphs. We show that the quality of the produced clusters is bounded by strong minimum cut and expansion criteria. We also develop a framework for hierarchical clustering and present applications to real-world data. We conclude that the clustering algorithms satisfy strong theoretical criteria and perform well in practice. IS - 4 JF - Internet Mathematics EP - 408 TI - Graph Clustering and Minimum Cut Trees VL - 1 SP - 385 KW - categories KW - dmoz KW - human KW - learning AU - Flake, Gary AU - Tarjan, Robert AU - Tsioutsiouliklis, Kostas UR - http://209.85.129.132/search?q=cache:nfa1pV010-gJ:www.internetmathematics.org/volumes/1/4/Flake.pdf+Graph+Clustering+and+Minimum+Cut+Trees&hl=en&ct=clnk&cd=1&client=iceweasel-a ER -