Abstract
Currently, with the high rate of generation of new information, it is important the traceability of its evolution. This paper studies techniques that allow analyzing the evolution of the knowledge, starting with analyzing the capabilities of the techniques to identify the patterns that represent the common information in datasets. From the “patterns,” the evolution of their characteristics over time is analyzed. The paper considers the next techniques for the problem of tracking the traceability of the patterns: LDA (Latent Dirichlet allocation), Birch (Balanced Iterative Reducing and Clustering using Hierarchies), LAMDA (Learning Algorithm for Multivariate Data Analysis), and K-means. They are used both for the initial task of grouping the data, as well as, to analyze the characteristics of the patterns, and the relevance of them in the patterns through their evolution (traceability). This paper uses different types of data sources of educational contents, and with these datasets, the topological models to describe the “patterns” generated from the grouping of the analyzed data, and their dynamics (evolution over time), are Studied (traceability). For the evaluation, the paper considers three metrics: Calinski–Harabasz Index, Davies–Bouldin Index, and Silhouette Score.
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Acknowledgements
This work has been supported by the project 64366: “Contenidos de aprendizaje inteligentes a través del uso de herramientas de Big Data, Analtica Avanzada e IA”—Ministry of Science—Government of Antioquia—Republic of Colombia.
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Aguilar, J., Salazar, C., Monsalve-Pulido, J., Montoya, E., Velasco, H. (2021). Traceability Analysis of Patterns Using Clustering Techniques. In: Arabnia, H.R., Ferens, K., de la Fuente, D., Kozerenko, E.B., Olivas Varela, J.A., Tinetti, F.G. (eds) Advances in Artificial Intelligence and Applied Cognitive Computing. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-70296-0_19
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