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Table 3: Comparison of NetScan and Greedy k-center.

in Abstract Joint Cluster Analysis of Attribute Data and Relationship Data: the Connected k-Center Problem
by Martin Ester, Rong Ge, Byron J. Gao, Zengjian Hu, Boaz Ben-moshe
"... In PAGE 10: ... We ran NetScan for the Connected k-Center problem and a known heuristic algorithm (Greedy k- center) [15] for the traditional k-center problem, both with k = 3. Table3 reports the clustering results av- eraged over 20 runs for both algorithms, recording the number of correctly identified professors for each com- munity together with the overall accuracy. To calculate the accuracy, we associated each of the three communi- ties with one of the clusters such that the best overall accuracy was achieved.... ..."

Table 4.1: Comparison of k-center methods

in Generalizations and Unification of Centroid-Based Clustering Methods
by Dr. Robert, E. Funderlic, Dr. Jon, Doyle Dr, Steffen Heber 2004

Table 1. Results

in Facility Location with Dynamic Distance Functions
by Yoram J. Sussmannz, Ding-zhu Du
"... In PAGE 4: ... We also provide approximation algorithms for several variations of the K-center problem and the K-suppliers problem for arbitrarily many time-slots, including weights and costs, with factors that are close to the best possible unless P = NP. These results are summarized in Table1 . The only known lower bound for anyoftheK-center problems is 2, the lower bound for the basic K-center problem.... ..."

Table 1. Results

in Facility Location with Dynamic Distance Functions
by Randeep Bhatia, Sudipto Guha, Samir Khuller, Y. J. Sussmann, Ding-zhu Du
"... In PAGE 4: ... We also provide approximation algorithms for several variations of the K-center problem and the K-suppliers problem for arbitrarily many time-slots, including weights and costs, with factors that are close to the best possible unless P = NP. These results are summarized in Table1 . The only known lower bound for any of the K-center problems is 2, the lower bound for the basic K-center problem.... ..."

Table 1. The speedup and efficiency for explicit Euler algorithm on PC cluster.

in FOR SEMIAUTOMATIC PARALLELIZATION OF FLOW IN POROUS MEDIA PROBLEM SOLVER
by A. Jaku ˇ Sev, V. Starikovičius, R. Čiegis 2005
"... In PAGE 5: ... A detailed description of the problem is given in [3]. In Table1 , the results of testing ParSol on PC cluster Vilkas of Vilnius Gediminas technical university are presented. PC clusters are known for their high communication latency thus the efficiency of parallel algorithms can be reduced for small computational problems when communication costs are relatively very high.... ..."

Table 1. The Efficient Graph Clustering Algorithm.

in Clustering and identifying temporal trends in document databases
by Rin Popescul, Gary William Flake, Steve Lawrence, Lyle H. Ungar, C. Lee Giles 2000
Cited by 20

Table 1. The Efficient Graph Clustering Algorithm.

in Clustering and identifying temporal trends in document databases
by Rin Popescul, Gary William Flake, Steve Lawrence, Lyle H. Ungar, C. Lee Giles 2000
Cited by 20

Table 1. The Efficient Graph Clustering Algorithm.

in Clustering and identifying temporal trends in document databases
by C. Lee, Giles Clustering, Identifying Temporal, Rin Popescul, Rin Popescul, Gary William Flake, Gary William Flake, Steve Lawrence, Steve Lawrence, Lyle H. Ungar, Lyle H. Ungar, C. Lee Giles 2000
Cited by 20

Table 1. The Efficient Graph Clustering Algorithm.

in Clustering and Identifying Temporal Trends in Document Databases
by Alexandrin Popescul Gary, Steve Lawrence, Lyle H. Ungar, C. Lee Giles

Table 1. The Efficient Graph Clustering Algorithm.

in Clustering and Identifying Temporal Trends in Document Databases
by Alexandrin Popescul, Gary Flake, Steve Lawrence, Lyle H. Ungar, C. Lee Giles
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