## 5 Discussion and Conclusions  
A central issue in pattern discovery research is the challenge of separating interesting patterns from trivial results. Patterns that are characteristic of a class, covering most examples in the class, may not be discriminant. On the other hand, highly discriminant patterns may have low coverage, may be sensitive to noise, and may be prone to overfit a few examples in the data [50]. In this paper, we studied, for a range of pattern interestingness measures and on real musicological data, the trade-off between completeness and discriminant power in mining characteristic patterns. The empirical findings confirm the considerations underlying different choices of measure: the typicality measure, used to discover characteristic patterns that cover all or most examples in a class [20; 30], achieves high completeness without controlling for discriminant power. The IC\({}^{++}\) measure was motivated by the view that those characteristic patterns are most interesting that also discriminate a target class from background classes [34]; consequently, class association patterns ranked high by the IC\({}^{++}\) measure are \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Measure** & **Class (Support)** & **Pattern (Support)** & \(n(X\wedge C)\) & \(I\) & \(\Gamma\) & \(\Delta\) \\ \hline B1 typicality & love songs (26) & tempoVoiceAccomp:noDrum (65) & 26 & 1.00 & 1.00 & 0.81 \\ B2 utility\({}_{a=0}\) & game songs (14) & tonality:minor, accidentals:none, firstPregression:upward, lastNoteReKey:keynote (25) & & 1.82 & 0.57 & 0.93 \\ B3 utility\({}_{a=1}\) & love songs (26) & tempoVoiceAccomp:noDrum (65) & 26 & 0.35 & 1.00 & 0.81 \\ B4 utility\({}_{a=2}\) & love songs (26) & structure:melodic (171) & 25 & 0.17 & 0.96 & 0.40 \\ B5 novelty & Mide’ songs (92) & structure:melodic, firstPregression:downward (121) & & 0.08 & 0.70 & 0.69 \\ B6 Laplace & war songs (87) & structure:mixed, lastNoteReKey:keynote, lastNoteReCompass:lowest, rhythmicUnits:one (12) & & 12 & 0.93 & 0.14 & 1.00 \\ B7 rel. risk & game songs (14) & lastNoteReKey:keynote, firstMetricPos:accented, rhythmicUnits:no (37) & & & & \\ B8 IC\({}^{++}\) & Mide’ songs (92) & firstPregression:downward (198) & 84 & 0.26 & 0.91 & 0.36 \\ B9 F1 score & Mide’ songs (92) & structure:melodic, firstPregression:downward (121) & & 0.60 & 0.70 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Top-ranked patterns (by interestingness measure) in Case Study 2, with class support count \(n(C)\), pattern support count \(n(X)\), association support count \(n(X\wedge C)\), interestingness \(I\) (according to the respective interestingness measure), completeness \(\Gamma\), and discriminant power \(\Delta\). generally more discriminant than patterns ranked high by typicality, while still retaining relatively high completeness. Similarly, novelty has been selected as an interestingness measure to discover patterns that are frequent in the target class but infrequent in the background, thus trading off sensitivity and specificity of the pattern [31]. Other measures, such as relative risk, suggested to prune patterns that are frequent in a class but not predictive of the class [21], tend to more strongly favor discriminant patterns at the cost of decreasing completeness. The results reported in this paper were also found to align with alternative uses of certain measures in contrast pattern mining (e.g., novelty) or pattern-based classification (e.g., Laplace estimate). Characteristic pattern discovery was applied to the [PERSON] collection of Native American music. In her publications, [PERSON] herself included quantitative analyses of collected songs organized into classes such as tribal repertoires or song types, which together with her narrative descriptions provide a valuable reference for assessing the results of the computational analysis.