  Thousands of first-millennium BCE ivory carvings have been excavated from
Neo-Assyrian sites in Mesopotamia (primarily Nimrud, Khorsabad, and Arslan
Tash) hundreds of miles from their Levantine production contexts. At present,
their specific manufacture dates and workshop localities are unknown. Relying
on subjective, visual methods, scholars have grappled with their classification
and regional attribution for over a century. This study combines visual
approaches with machine-learning techniques to offer data-driven perspectives
on the classification and attribution of this early Iron Age corpus. The study
sample consisted of 162 sculptures of female figures. We have developed an
algorithm that clusters the ivories based on a combination of descriptive and
anthropometric data. The resulting categories, which are based on purely
statistical criteria, show good agreement with conventional art historical
classifications, while revealing new perspectives, especially with regard to
the contested Syrian/South Syrian/Intermediate tradition. Specifically, we have
identified that objects of the Syrian/South Syrian/Intermediate tradition may
be more closely related to Phoenician objects than to North Syrian objects; we
offer a reconsideration of a subset of Phoenician objects, and we confirm
Syrian/South Syrian/Intermediate stylistic subgroups that might distinguish
networks of acquisition among the sites of Nimrud, Khorsabad, Arslan Tash and
the Levant. We have also identified which features are most significant in our
cluster assignments and might thereby be most diagnostic of regional carving
traditions. In short, our study both corroborates traditional visual
classification methods and demonstrates how machine-learning techniques may be
employed to reveal complementary information not accessible through the
exclusively visual analysis of an archaeological corpus.
