{"ID":2864810,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19162","arxiv_id":"2511.19162","title":"BioArtlas: Computational Clustering of Multi-Dimensional Complexity in Bioart","abstract":"Bioart's hybrid nature spanning art, science, technology, ethics, and politics defies traditional single-axis categorization. I present BioArtlas, analyzing 81 bioart works across thirteen curated dimensions using novel axis-aware representations that preserve semantic distinctions while enabling cross-dimensional comparison. Our codebook-based approach groups related concepts into unified clusters, addressing polysemy in cultural terminology. Comprehensive evaluation of up to 800 representation-space-algorithm combinations identifies Agglomerative clustering at k=15 on 4D UMAP as optimal (silhouette 0.664 +/- 0.008, trustworthiness/continuity 0.805/0.812). The approach reveals four organizational patterns: artist-specific methodological cohesion, technique-based segmentation, temporal artistic evolution, and trans-temporal conceptual affinities. By separating analytical optimization from public communication, I provide rigorous analysis and accessible exploration through an interactive web interface (https://www.bioartlas.com) with the dataset publicly available (https://github.com/joonhyungbae/BioArtlas).","short_abstract":"Bioart's hybrid nature spanning art, science, technology, ethics, and politics defies traditional single-axis categorization. I present BioArtlas, analyzing 81 bioart works across thirteen curated dimensions using novel axis-aware representations that preserve semantic distinctions while enabling cross-dimensional comp...","url_abs":"https://arxiv.org/abs/2511.19162","url_pdf":"https://arxiv.org/pdf/2511.19162v2","authors":"[\"Joonhyung Bae\"]","published":"2025-09-27T17:26:17Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.CY\",\"cs.HC\",\"cs.LG\",\"cs.MM\"]","methods":"[\"LoRA\",\"Generative Adversarial Network\"]","project_urls":"[\"https://www.bioartlas.com\"]","has_code":false,"code_links":[{"ID":609204,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2864810,"paper_url":"https://arxiv.org/abs/2511.19162","paper_title":"BioArtlas: Computational Clustering of Multi-Dimensional Complexity in Bioart","repo_url":"https://github.com/joonhyungbae/BioArtlas","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}