Unsupervised Spectral Analysis of Bio-Dyed Textile Samples

Zong-Yue Li; Joni Hyttinen; Riikka Räisänen; Xiao-Zhi Gao; Markku Hauta-Kasari

doi:10.53941/ijndi.2023.100001

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.

References

1.
Lohtander, T.; Arola, S.; Laaksonen, P. Biomordanting willow bark dye on cellulosic materials. Color. Technol., 2020, 136: 3−14.
2.
Rahaman, G.M.A.; Parkkinen, J.; Hauta-Kasari, M. A novel approach to using spectral imaging to classify dyes in colored fibers. Sensors (Basel) 2020, 20, 4379. doi:10.3390/s20164379
3.
Pearson, K. LIII. On lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin Philos. Mag. J. Sci., 1901, 2: 559−572.
4.
Hotelling, H. Analysis of a complex of statistical variables into principal components. J. Educ. Psychol., 1933, 24: 498−520.
5.
Arabie, P.; Hubert, L.; De Soete, G. Clustering and Classification; World Scientific: River Edge, 1996.
6.
Davies D. L.; Bouldin, D.W. A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1979, PAMI-1, 224–227. doi:10.1109/TPAMI.1979.4766909
7.
Rousseeuw, P.J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math., 1987, 20: 53−65.
8.
MacQueen, J. Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, 21 June-18 July 1965 and 27 December 1965-7 January 1966; Statistical Laboratory of the University of California: Berkeley, 1967; pp. 281–297.
9.
Bezdek, J.C. A convergence theorem for the fuzzy ISODATA clustering algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 1980, PAMI-2, 1–8. doi:10.1109/TPAMI.1980.4766964
10.
Ester, M.; Kriegel, H.P.; Sander, J.; et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, 2–4 August 1996; AAAI Press: Portland, 1996; pp. 226–231.
11.
Ankerst, M.; Breunig, M.M.; Kriegel, H.P.; et al. OPTICS: Ordering points to identify the clustering structure. ACM SIGMOD Rec., 1999, 28: 49−60.
12.
Kohonen, T. The self-organizing map. Proc. IEEE, 1990, 78: 1464−1480.
13.
Kohonen, T.; Oja, E.; Simula, O.; et al. Engineering applications of the self-organizing map. Proc. IEEE, 1996, 84: 1358−1384.
14.
Merkl D.; Rauber, A. Uncovering the hierarchical structure of text archives by using an unsupervised neural network with adaptive architecture. In Knowledge Discovery and Data Mining. Current Issues and New Applications; Terano, T.; Liu, H.; Chen, A.L.P., Eds.; Springer: Berlin Heidelberg, 2000; pp. 384–395.

Scilight Press

Author Information

Abstract

Graphical Abstract

Keywords

References

About Scilight

Journals

Publishing Policies

Contact Us