Optimizing the Descendant-Aware Clustering Parameters
Sukhwan Jung, Aviv Segev · 2022 IEEE International Conference on Big Data (Big Data) · 2022
Topic evolution is a recently introduced field of research as a substitute for a more traditional text-based topic evolution, allowing the tracking of more complex evolutionary events with the use of network structures. Network-based topic evolution showed that the neighborhood characteristics of newly introduced topics can be utilized to determine when a topic would emerge in a given domain. Predicting emerging topics requires a method for generating pseudo-neighbors of previously unseen topics as the neighborhood for an emerging topic is not known before its appearance. The authors proposed the Descendant-Aware Clustering algorithm to generate a set of neighborhood candidates for future emerging topics, surpassing existing algorithms in both performances and computation times. Optimizing the algorithm parameters enhances the performance even further. Significant performance improvements were observed when NSGA-III multi-objective algorithm was applied to over 100 research domains. A set of enhanced default values are introduced to the proposed algorithm removing the necessity for dataset-specific optimization, cementing the position of the Descendant-Aware Clustering as the best clustering algorithm for detecting ancestors of future emerging topics.