Analyzing large data sets in reasonable times: Solutions for composite optima
Analyzing large data sets in reasonable times: Solutions for composite optima
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DOI：
10.1111/j.1096-0031.1999.tb00278.x
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发表时间：
1999-12-01
期刊：
影响因子：
3.6
通讯作者：
Goloboff, PA
中科院分区：
文献类型：
--
作者：
Goloboff, PA
提出了大数据集简约分析的新方法。新方法是扇区搜索、树漂移和树融合。对于 Chase 等人的 500 个分类单元数据集，这些方法（在 266 MHz Pentium II 上）可在 10 分钟内找到最短的树（即，比 PAUP 快 15,000 倍以上，比 PAUP* 快 1000 倍）。进行完整的简约分析需要多次独立地达到最小长度，但不一定是所有“岛屿”；对于 Chase 等人的数据集，这可以在 4 到 6 小时内完成。新方法在其他分析案例（范围从 170 到 854 个分类单元）中也表现良好。 (C) 1999 年威利亨尼格协会。
New methods for parsimony analysis of large data sets are presented. The new methods are sectorial searches, tree-drifting, and tree-fusing. For Chase et nl.'s 500-taxon data set these methods (on a 266-MHz Pentium II) find a shortest tree in less than 10 min (i.e., over 15,000 times faster than PAUP and 1000 times faster than PAUP*). Making a complete parsimony analysis requires hitting minimum length several times independently, but not necessarily all "islands"; for Chase et al.'s data set, this can be done in 4 to 6 h. The new methods also perform well in other cases analyzed (which range from 170 to 854 taxa). (C) 1999 The Willi Hennig Society.