BioArtlas: Computational Clustering of Multi-Dimensional Complexity in Bioart

TL;DR

BioArtlas is a novel computational framework that analyzes complex bioart works across multiple dimensions, revealing organizational patterns and enabling accessible exploration through an interactive web interface.

Contribution

It introduces a new axis-aware clustering approach for multi-dimensional bioart analysis, with an optimal clustering method and publicly available dataset and tools.

Findings

Optimal clustering identified as Agglomerative at k=15 on 4D UMAP

Revealed four organizational patterns in bioart works

Provided an interactive web interface for exploration

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).