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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.
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BMC Ecology and Evolution

ISSN: 2730-7182

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