Browsing by Author "Gomez-Nieto, E."

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    ICE: A visual analytic tool for interactive clustering ensembles generation
    (Association for Computing Machinery, 2021) Castro-Ochante, J.; Camara-Chavez, G.; Gomez-Nieto, E.
    "Clustering methods are the most used algorithms for unsupervised learning. However, there is no unique optimal approach for all datasets since different clustering algorithms produce different partitions. To overcome this issue of selecting an appropriate technique and its corresponding parameters, cluster ensemble strategies are used for improving accuracy and robustness by a weighted combination of two or more approaches. However, this process is often carried out almost in a blind manner, testing different combinations of methods and assessing if its performance is beneficial for the defined purpose. Thus, the procedure for selecting the best combination tests many clustering ensembles until the desired result is achieved. This paper proposes a novel analytic tool for clustering ensemble generation, based on quantitative metrics and interactive visual resources. Our approach allows the analysts to display different results from state-of-the-art clustering methods and analyze their performance based on specific metrics and visual inspection. Based on their requirements/experience, the analysts can interactively assign weights to the different methods to set their contributions and manage (create, store, compare, and merge), such as for ensembles. Our approach's effectiveness is shown through a set of experiments and case studies, attesting to its usefulness in practical applications. © 2021 ACM."
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    Political discourses, ideologies, and online coalitions in the Brazilian Congress on Twitter during 2019
    (SAGE Publications Ltd, 2021) García-Sánchez, E.; Benetti, P.R.; Higa, G.L.; Alvarez, M.C.; Gomez-Nieto, E.
    "The aim of this research is to describe the pattern of interactions of Brazilian legislators on Twitter during 2019 in the construction of political discourses. Based on 20,076 replies during 2019, posted on Twitter by 514 Brazilian legislators, we conducted descriptive analysis of legislators’ Twitter profiles, social network analyses from their interactions, and content analysis of the messages. We found that (1) there are large disparities between legislators in the use of Twitter; (2) the pattern of interactions depicted five clusters defined by political affinities; (3) each cluster had different features regarding their composition and impact; (4) the centrality of the legislators within the network was positively associated with public endorsement on Twitter; and (5) the topics of messages within the clusters reinforce discourses aligned to political ideologies. We argue that the pattern of interactions on Twitter allows to identify online coalitions that reinforce particular discourses within the Brazilian parliamen"