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Table 3 Analysis times (seconds) of different algorithms on different datasets, and using different information theoretic metrics to choose partitioning schemes

From: Selecting optimal partitioning schemes for phylogenomic datasets

  AICc BIC
Dataset Greedy Relaxed clustering Strict clustering Greedy Relaxed clustering Strict clustering
Ward_2010 396 42 58 587 56 58
Wainwright_2012 3305 400 603 5664 568 603
Pyron_2011 602 58 74 790 73 74
Li_2008 1246 130 165 1557 194 165
Leavitt_2013 5829 843 288 7997 973 288
Kaffenberger_2011 580 78 104 877 102 104
Irisarri_2012 935 87 112 1172 134 112
Hackett_2008 102011 9536 3140 130359 12686 3140
Fong_2012 10468 987 183 13961 1094 183
Endicott_2008 1947 189 126 2135 207 126
  1. The two clustering algorithms are roughly an order of magnitude faster than the greedy algorithm. Analyses were conducted on a Mac Pro with 2 2.26GHz Quad-Core Intel Xeon processors and 32 GB RAM.