Abstract
This paper presents a projection-based clustering method for visualizing high-dimensional data points in lower-dimensional spaces while preserving the data’s structural properties. The proposed method modifies the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm by adding a weight function that adjusts the dissimilarity between high-dimensional data points to obtain more realistic lower-dimensional representations. In our algorithm, the centroids obtained with a prototype-based clustering algorithm attract high-dimensional data points allocated to their respective clusters, while repelling those points assigned to other clusters. The simulations using real-world datasets show that the Weighted t-SNE produces better projections than similar algorithms without the need for any previous dimensionality reduction step.
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Nápoles, G., Concepción, L., Özgöde Yigin, B., Saygili, G., Vanhoof, K., Bello, R. (2024). Weighted t-Distributed Stochastic Neighbor Embedding for Projection-Based Clustering. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2023. Lecture Notes in Computer Science, vol 14335. Springer, Cham. https://doi.org/10.1007/978-3-031-49552-6_12
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