Percorrer por autor "Rocha, Ana Maria A. C."
A mostrar 1 - 3 de 3
Resultados por página
Opções de ordenação
- A collaborative multi-objective approach for clustering task based on distance measures and clustering validity indicesPublication . Azevedo, Beatriz Flamia; Rocha, Ana Maria A. C.; Pereira, Ana I.Clustering algorithm has the task of classifying a set of elements so that the elements within the same group are as similar as possible and, in the same way, that the elements of different groups (clusters) are as different as possible. This paper presents the Multi-objective Clustering Algorithm (MCA) combined with the NSGA-II, based on two intra- and three inter-clustering measures, combined 2-to-2, to define the optimal number of clusters and classify the elements among these clusters. As the NSGA-II is a multi-objective algorithm, the results are presented as a Pareto front in terms of the two measures considered in the objective functions. Moreover, a procedure named Cluster Collaborative Indices Procedure (CCIP) is proposed, which aims to analyze and compare the Pareto front solutions generated by different criteria (Elbow, Davies-Bouldin, Calinski-Harabasz, CS, and Dumn indices) in a collaborative way. The most appropriate solution is suggested for the decision-maker to support their final choice, considering all solutions provided by the measured combination. The methodology was tested in a benchmark dataset and also in a real dataset, and in both cases, the results were satisfactory to define the optimal number of clusters and to classify the elements of the dataset.
- Multi-objective clustering algorithm applied to the mathE categorization problemPublication . Azevedo, Beatriz Flamia; Leite, Gabriel A.; Pacheco, Maria F.; Fernandes, Florbela P.; Rocha, Ana Maria A. C.; Pereira, Ana I.This work explores bio-inspired strategies and clustering techniques to propose an automatic clustering algorithm, named Multi-objective Clustering Algorithm (MCA). This algorithm uses a set of measure combinations to define the optimal number of clusters and the partitioning of the elements, minimizing an intra-clustering measure and maximizing an inter-clustering one. The MathE platform is an educational tool whose main objective is to assist students facing challenges in Mathematics at higher education level. Based on previous studies, the opinions of lecturers and students diverge regarding the difficulty level of the questions available on the platform. Therefore, this research aims to explore and develop a new clustering method for question categorization, taking into account the opinions of both lecturers and students about the difficulty levels of the questions. The Multi-objective Clustering Algorithm (MCA) is proposed to group the questions into clusters representing the difficulty level of the platform's questions. Compared with the k-means algorithm, the MCA results exhibit outstanding performance. Through a combination of multi-objective clustering measures, the MCA successfully achieved a set of optimal solutions (Hybrid Pareto front). This method empowers the decision-maker, enabling them to choose the most appropriate solution based on additional insights beyond the model.
- A Multi-objective Clustering Algorithm Integrating Intra-Clustering and Inter-Clustering MeasuresPublication . Azevedo, Beatriz Flamia; Rocha, Ana Maria A. C.; Pereira, Ana I.This study delves into bio-inspired approaches and clustering methodologies to introduce an automated clustering algorithm named Multi-objective Clustering Algorithm (MCA). Using multi-objective strategies and several combination measures, this method calculates the optimal number of clusters and element partitioning by minimizing intra-clustering measures and maximizing inter-clustering ones. Through experimentation on three benchmark datasets, the results highlight the success of the MCA in obtaining a set of optimal solutions (Hybrid Pareto front) through the integration of multi-objective strategies and clustering measures. Moreover, the Dunn clustering validity index is used to support the decision maker in selecting the optimal solution among the ones presented in the Hybrid Pareto front. This approach allows decision-makers to choose the most suitable solution by incorporating additional insights beyond the model.