A Clustering Validation Index Based on Semantic Description

Abstract

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.

Vitor V. Curtis

Researcher

My research interests include high performance computing, algorithms, and optimization.

Filipe A. N. Verri

Researcher

My research interests include data science, machine learning, complex networks, and complex systems.