## 1 Introduction  
Advances in music data mining and the creation of annotated music corpora [1; 2; 3] have supported a renewed interest in comparative music analysis [4; 5]. Analyses of class-labeled music datasets have explored a range of data mining paradigms, including descriptive methods such as subgroup discovery and emerging pattern mining [6; 7; 8; 9; 10; 11; 12] and predictive methods such as decision tree and classification rule induction [13; 14; 15; 16; 17; 18]. These studies generally focus on identifying discriminant properties, which distinguish different classes. Highly discriminant patterns, however, may only describe a few examples in a class. In fact, emerging pattern mining will discover contrasts between classes even if patterns are infrequent [19]; applications to music corpora consequently reveal emerging patterns that sometimes cover only a small proportion of examples in a class [12]. In contrast to discriminant patterns, characteristic patterns capture properties that are prevalent in a class: ideally, a characteristic pattern is complete, i.e., it covers all, or almost all, examples of a class [20]; completeness does not require characteristic patterns to also discriminate between classes. Other methods for characteristic pattern discovery consider characteristic patterns the more interesting the more specific they are to a certain class [21; 22]. This paper explores characteristic pattern mining for music corpus analysis and investigates the trade-off between completeness and discriminant power of descriptive patterns.  
Two examples shall illustrate characteristic patterns in music. These examples are based on analyses by [PERSON] (1867-1957), one of the most prolific collectors of Native American music. Through comparative analyses of feature distributions in different tribal repertoires, [PERSON] sought to identify common and distinctive properties of these repertoires. The two selected patterns are both shared by a majority of songs in a class: among the songs of the Chippewa, 67% end on the keynote; of Teton Sioux songs, 70% contain one or more rhythmic units. However, when compared against songs of other tribes (Ute, Mandan, Hidatsa, Papago, Pawnee, and Menominee), only the first of these patterns shows different distributions in different tribes: \"Tribes differ in the location of the keynote; for example, the Chippewa usually build their melodies above the keynote, while the Papago more frequently place the melody partly above and partly below the keynote. The percentage of Chippewa songs ending on the keynote is 67, while the Papago group contains 41 per cent with this ending\" [23] (pp. 19-20). The occurrence of rhythmic units, on the other hand, is prevalent not only in Sioux music, but also in the songs of other tribes: \"The tribes show little difference in this respect [use of a rhythmic unit], and 72 per cent of the entire group contains one or more rhythmic units\" [23] (p. 20). Thus, both patterns are characteristic for their class (describing a majority of songs in the class), but the ending on the keynote is more discriminant (describing proportionately fewer songs in other classes) than the use of rhythmic units (Figure 1). [PERSON]'s analyses present a remarkable opportunity to compare, qualitatively, the results of computational pattern mining to published musicological findings.  
To separate potentially interesting patterns from trisul results, different pattern interestingness measures have been proposed [24]. In this paper, we analyze interestingness measures for characteristic patterns, investigating their ability to balance class coverage and discriminant power of patterns (Section 2). Top \(k\) pattern mining is applied to the [PERSON] collection of Native American music (Section 3); the behavior of top \(k\) patterns mined with different interestingness measures is compared, and top ranked example patterns are discussed with reference to observations by [PERSON] (Section 4).