# Identification and Description of Outliers in the [PERSON] Collection of Native American Music
## 5 Discussion and Conclusions  
The method introduced in this paper integrates outlier detection and description in grouped data based on infrequent contrast patterns. The outlier identification is unsupervised: the method is used for exploratory analysis of a music collection in which neither outliers nor inliers are known a priori. Existing work in outlier detection has exploited both frequent and infrequent patterns: an example is less likely an outlier if it satisfies frequent patterns [48], or more likely an outlier if it satisfies infrequent patterns [49] or violates frequent patterns [50]. Despite the suitability of patterns for descriptive analysis, however, among the mentioned methods only FP-Outlier [48] explicitly includes outlier description: while the identification of outliers is based on frequent patterns _satisfied_ by an example--with a larger number of satisfied frequent patterns making the example less likely to be an outlier--the description is based on frequent patterns _not satisfied_ by the example (contradicting patterns), i.e., identification and description are based on different sets of patterns. In contrast, our method elegantly uses the same significantly infrequent patterns to both identify and describe outliers. Furthermore, our analysis interest is complementary to existing work on pattern-based outlier detection in grouped data. On the one hand, as unsupervised outlier identification the method differs from supervised or semi-supervised approaches which contrast a target group (known outliers or unlabelled examples) against a background assumed to contain no outliers (examples labelled 'normal') [51; 52; 53]. On the other hand, among unsupervised outlier detection methods it focuses on attribute outliers, i.e., examples with unusual attribute values or attribute-value combinations in the context of their group, rather than class outliers or class noise, i.e., examples with a deviating and potentially erroneous group label [54]. Applications of outlier detection to music generally assume numeric input data and employ instance- or model-based outlier detection methods [40; 41; 42; 43]. In contrast to the pattern-based outlier analysis proposed here, the existing applications lack an explicit description of outlying examples. Within computational ethnomusicology, our method builds on research in contrast pattern mining [55]. In particular, it complements work on antipatterns, i.e., patterns over-represented in the background or anticorpus [35]: while the earlier study illustrated that exceptions to antipatterns might reveal erroneous or ambiguous class labelling (class outliers), here songs described by infrequent patterns reveal properties which are unusual within their class (attribute outliers). In this context, [PERSON]'s collection and analyses provide an invaluable opportunity to qualitatively assess outlier identification for exploratory analysis of music collections. The fact that many discovered outliers can be directly or indirectly matched to related observations by [PERSON] indicates that the proposed method is effective in identifying songs with unusual properties. Beyond [PERSON]'s manual analysis, computational outlier detection and description facilitates transparent discovery of infrequent feature combinations in addition to infrequent single features. A further extension to [PERSON]'s analysis concerns the evaluation of pattern candidates, which employs a statistical test to discover significantly infrequent patterns. Descriptions of outlying songs and their groups depend on the chosen feature vocabulary. The study presented here has adopted [PERSON]'s own content descriptors, thus allowing to compare results of the computational analysis with [PERSON]'s observations. Digital encoding of the Densmore  
Figure 6: (**Top**): Chippewa lullaby ID102, recorded on the White Earth Reservation ([7], p. 163). (**Bottom**): Chippewa lullaby ID447, recorded on the Red Lake (Lac du Flambeau) Reservation ([8], p. 241).