2021/01/01 by Nejat Arinik, Nejat Arınık, Vincent Labatut +1
Agricultural and Biological Sciences · Computer Science · Mathematics · Physics and Astronomy · #Advanced Clustering Algorithms Research #Cluster (spacecraft) #Complex Network Analysis Techniques #Computer science #Context (archaeology) #Data mining #Field (mathematics) #Machine learning #Mathematics #Measure (data warehouse) #Parametric statistics #Partition (number theory) #Relevance (law) #Selection (genetic algorithm) #Sensory Analysis and Statistical Methods #Set (abstract data type) #Statistics #Task (project management) #cs.LG #physics.data-an
paper · pdf · doi:10.1109/access.2021.3054621
published as IEEE Access 9:20255-20276, 2021 · IEEE Access, IEEE, 2021
openalex publication_date 2021/01/01 · arxiv created 2021/02/01 · arxiv updated 2021/02/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
In the context of cluster analysis and graph partitioning, many external evaluation measures have been proposed in the literature to compare two partitions of the same set. This makes the task of selecting the most appropriate measure for a given situation a challenge for the end user. However, this issue is overlooked in the literature. Researchers tend to follow tradition and use the standard measures of their field, although they often became standard only because previous researchers started consistently using them. In this work, we propose a new empirical evaluation framework to solve this issue, and help the end user selecting an appropriate measure for their application. For a collection of candidate measures, it first consists in describing their behavior by computing them for a generated dataset of partitions, obtained by applying a set of predefined parametric partition transformations. Second, our framework performs a regression analysis to characterize the measures in terms of how they are affected by these parameters and transformations. This allows both describing and comparing the measures. Our approach is not tied to any specific measure or application, so it can be applied to any situation. We illustrate its relevance by applying it to a selection of standard measures, and show how it can be put in practice through two concrete use cases.