부산대학교 도서관

The paper illustrates how the application of a specific version of Artificial Neural Networks, Self-Organizing Maps (SOMs), enabled a more accurate analysis of human dental morphology. SOMs enable the processing of individual samples (dentitions) because they can cope with missing data. In fact, in archaeological samples of human remains, teeth are often broken or missing making a complete set of morphological traits often impossible to achieve. Other classification methods like Principal Component Analysis, Multidimensional Scaling, Mean Measure of Divergence, Multiple Correspondence Analysis do not handle missing descriptors and incomplete data matrices have to be filled in, thus leading to a certain approximation in the outcome with a lack of geographical or temporal resolution, as many incomplete samples have to be merged into a virtual one that does not present missing descriptors. Our discussion about the proficiency of SOMs, and ANNs in general, in the exploration and classification of anthropological databases concerning morphology is based on a specific case study, that is the classification of a Neanderthal sample. Through this example we would like to attract the attention of anthropologists and archaeologists to a very flexible methodology that is seldom applied, despite being widely used in many other disciplines.