New approach based on fuzzy logic and principal component analysis for the classification of two-dimensional maps in health and disease

2003 ◽  
Vol 1004 (1-2) ◽  
pp. 13-28 ◽  
Author(s):  
Emilio Marengo ◽  
Elisa Robotti ◽  
Pier Giorgio Righetti ◽  
Francesca Antonucci
Cosmetics ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 11
Author(s):  
Manuel Zarzo

In order to guide consumers in their purchase of a new fragrance, one approach is to visualize the spectrum of men’s or women’s fragrances on a two-dimensional plot. One of such sensory maps available is the Hexagon of Fragrance Families. It displays 91 women’s perfumes inside a polygon, so that each side accounts for a different olfactory class. In order to discuss this chart, odor profiles were obtained for these fragrances and additional feminine ones (140 in total, launched from 1912 to 1990). An olfactory dataset was arranged by coding numerically the descriptions obtained from Fragrantica and Osmoz websites, as well as from a perfume guide. By applying principal component analysis, a sensory map was obtained that properly reflected the similarities between odor descriptors. Such representation was equivalent to the map of feminine fragrances called Givaudan Analogies, comprised of five major categories. Based on the results, a modified version of the Hexagon based on 14 categories was proposed. The first principal component explained preference for daytime versus nighttime wear, and regression models were fitted in order to estimate such preferences according to the odor profiles. The second component basically discriminated floral versus chypre (mossy–woody) fragrances. Results provide a fundamental basis to develop standard sensory maps of women’s fragrances.


Author(s):  
Hyeuk Kim

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.


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