Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives
This work addresses the challenge of efficiently clustering massive publication datasets for researchers and analysts, but it is incremental as it builds on existing fuzzy clustering and optimization techniques.
The paper tackles the problem of fuzzy clustering for large-scale scientific article databases like OpenAlex and Web of Science, which contain about 70 million articles and a billion citations, by formulating it as a constrained optimization model and proposing GPU-accelerated methods to handle the computational challenges.
Fuzzy clustering, which allows an article to belong to multiple clusters with soft membership degrees, plays a vital role in analyzing publication data. This problem can be formulated as a constrained optimization model, where the goal is to minimize the discrepancy between the similarity observed from data and the similarity derived from a predicted distribution. While this approach benefits from leveraging state-of-the-art optimization algorithms, tailoring them to work with real, massive databases like OpenAlex or Web of Science - containing about 70 million articles and a billion citations - poses significant challenges. We analyze potentials and challenges of the approach from both mathematical and computational perspectives. Among other things, second-order optimality conditions are established, providing new theoretical insights, and practical solution methods are proposed by exploiting the structure of the problem. Specifically, we accelerate the gradient projection method using GPU-based parallel computing to efficiently handle large-scale data.