To fufill an Advanced Topics in Research Methods, I completed the Machine Learning course from Parsons’ Arts, Media and Technology department. This course explored logistical regression sentiment analysis, image recognition models, and k-means clustering.
The clustering project explored with unsupervised learning methods to cluster fine art from museum collections and galleries on metadata characteristics. To group the artworks to be as visually similar as possible, I tuned the model to cluster acccording to value, hue, texture, and spacial dimension.
When selecting the number of clusters to proceed with in a K-means analysis, the number of clusters should minimize negative decision values, below average silhouette scores and also due to wide fluctuations in the size of the silhouette plots. I proceeded with 10 clusters as the plots showed a smaller difference in size of clusters than in other breakdowns.
Looking at these variables provided a lot of insight into characteristics like depth and form of the works. I developed curator descriptions for each collection, drawing on visual similarities in each cluster.