Generating similarity cluster of Indonesian languages with semi-supervised clustering - Arbi Haza Nasution; Yohei Murakami; Toru Ishida (Institute of Advanced Engineering and Science, 2019)
Lexicostatistic and language similarity clusters are useful for computational linguistic researches that depends on language similarity or cognate recognition. Nevertheless, there are no published lexicostatistic/language similarity cluster of Indonesian ethnic languages available. We formulate an approach of creating language similarity clusters by utilizing ASJP database to generate the language similarity matrix, then generate the hierarchical clusters with complete linkage and mean linkage clustering, and further extract two stable clusters with high language similarities. We introduced an extended k-means clustering semi-supervised learning to evaluate the stability level of the hierarchical stable clusters being grouped together despite of changing the number of cluster. The higher the number of the trial, the more likely we can distinctly find the two hierarchical stable clusters in the generated k-clusters. However, for all five experiments, the stability level of the two hierarchical stable clusters is the highest on 5 clusters. Therefore, we take the 5 clusters as the best clusters of Indonesian ethnic languages. Finally, we plot the generated 5 clusters to a geographical map.