Unsupervised Spectral Analysis of Bio-Dyed Textile Samples

Abstract

Article Unsupervised Spectral Analysis of Bio-Dyed Textile Samples Zong-Yue Li 1,*, Joni Hyttinen 1, Riikka Räisänen 2, Xiao-Zhi Gao 1, and Markku Hauta-Kasari 1 1 School of Computing, Faculty of Science, Forestry and Technology, University of Eastern Finland, Joensuu, 80101, Kuopio, 70211, Finland 2 Department of Education, Faculty of Educational Sciences, University of Helsinki, Helsinki, 00014 , Finland * Correspondence: winstonli711@gmail.com Received: 12 March 2023 Accepted: 23 March 2023 Abstract: Natural compounds such as biological colorants (biocolorants) have long been employed as crucial ingredients for dying textile in the textile industry. As one part of the BioColour Consortium project, our goal is to take advantage of machine learning (in cluster analysis) to discover possible clusters of bio-dyed textile in the absence of ground truth labels or other knowledge of expert domains. Specifically, we use unsupervised learning methods of agglomerative clustering, fuzzy c-means, ordering points to identify the clustering structure (OPTICS) and self-organizing maps (SOMs), resulting in an investigation that combines data visualization and cluster analysis. In summary, we apply some selected data mining methods to 1) discover hidden clusters emerging among products that are colored with biocolorant (specifically bio-dyed textile samples), and 2) show the potentials of clustering techniques in the case study.