A Clustering Validation Index Based on Semantic Description


In clustering problems where the objective is not based on specifically spatial proximity, but rather on feature patterns and the semantic description, traditional internal cluster validation indices might not be appropriate. This article proposes a novel validity index to suggest the most appropriate number of clusters based on a semantic description of categorical databases. To assess our index, we also propose a synthetic data generator specifically designed for this type of application. We tested data sets with different configurations to assess the performance of the proposed index compared to well-known indices in the literature. Thus, we demonstrate that the index has great potential for discovering the number of clusters for the type of application studied and the data generator is able to produce relevant data sets for the internal validation process.
AQUINO, Roberto Douglas Guimarães de; CURTIS, Vitor Venceslau; VERRI, Filipe Alves Neto. A Clustering Validation Index Based on Semantic Description. In: BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), 12. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 315-328. ISSN 2643-6264.