Analyzing phonetic variation in the traditional English dialects:Simultaneously clustering dialects and phonetic features

This study explores the linguistic application of bipartite spectral graph partitioning, a graph-theoretic technique that simultaneously identifies clusters of similar localities as well as clusters of features characteristic of those localities. We compare the results using this approach with previously published results on the same dataset using cluster and principal component analysis (Shackleton 2007). Although the results of the spectral partitioning method and Shackleton's approach overlap to a broad extent, the analyses offer complementary insights into the data. The traditional cluster analysis detects some clusters that are not identified by the spectral partitioning analysis, whereas the reverse also occurs. Similarly, the principal component analysis and the spectral partitioning analysis detect many overlapping but also some different linguistic variants. The main benefit of the bipartite spectral graph partitioning method over the alternative approaches remains its ability to simultaneously identify sensible geographical clusters of localities with their corresponding linguistic features.