In computational pattern discovery, pattern evaluation measures select or rank patterns according to their potential interestingness in a given analysis task. Many measures have been proposed to accommodate different pattern types and properties. This paper presents a method and case study employing measures for frequent, characteristic, associative, contrasting, dependent, and significant patterns to model pattern interestingness in a reference analysis, Frances Densmore's study of Teton Sioux songs. Results suggest that interesting changes from older to more recent Sioux songs according to Densmore's analysis are best captured by contrast, dependency, and significance measures.

在计算模式发现中，模式评估措施根据模式在给定分析任务中的潜在兴趣来选择或排序模式。已经提出了许多措施来适应不同的模式类型和属性。本文介绍了一种方法和案例研究，该方法和案例研究采用对频繁、特征、关联、对比、依赖和重要模式的度量来模拟参考分析中的模式趣味性，即弗朗西斯·登斯莫尔 (Frances Densmore) 对 Teton Sioux 歌曲的研究。结果表明，根据 Densmore 的分析，从较旧的苏族歌曲到较新的苏族歌曲的有趣变化最好通过对比度、依赖性和重要性度量来捕捉。