  The humanities, like many other areas of society, are currently undergoing
major changes in the wake of digital transformation. However, in order to make
collection of digitised material in this area easily accessible, we often still
lack adequate search functionality. For instance, digital archives for textiles
offer keyword search, which is fairly well understood, and arrange their
content following a certain taxonomy, but search functionality at the level of
thread structure is still missing. To facilitate the clustering and search, we
introduce an approach for recognising similar weaving patterns based on their
structures for textile archives. We first represent textile structures using
hypergraphs and extract multisets of k-neighbourhoods describing weaving
patterns from these graphs. Then, the resulting multisets are clustered using
various distance measures and various clustering algorithms (K-Means for
simplicity and hierarchical agglomerative algorithms for precision). We
evaluate the different variants of our approach experimentally, showing that
this can be implemented efficiently (meaning it has linear complexity), and
demonstrate its quality to query and cluster datasets containing large textile
samples. As, to the est of our knowledge, this is the first practical approach
for explicitly modelling complex and irregular weaving patterns usable for
retrieval, we aim at establishing a solid baseline.
