Examinando por Materia "Unsupervised learning"
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Ítem An Automatic Merge Technique to Improve the Clustering Quality Performed by LAMDA(Institute of Electrical and Electronics Engineers Inc., 2020-01-01) Morales, Luis; Aguilar, Jose; Morales, Luis; Aguilar, Jose; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las ComunicacionesClustering is a research challenge focused on discovering knowledge from data samples whose goal is to build good quality partitions. In this paper is proposed an approach based on LAMDA (Learning Algorithm for Multivariable Data Analysis), whose most important features are: a) it is a non-iterative fuzzy algorithm that can work with online data streams, b) it does not require the number of clusters, c) it can generate new partitions with objects that do not have enough similarity with the preexisting clusters (incremental-learning). However, in some applications, the number of created partitions does not correspond with the number of desired clusters, which can be excessive or impractical for the expert. Therefore, our contribution is the formalization of an automatic merge technique to update the cluster partition performed by LAMDA to improve the quality of the clusters, and a new methodology to compute the Marginal Adequacy Degree that enhances the individual-cluster assignment. The proposal, called LAMDA-RD, is applied to several benchmarks, comparing the results against the original LAMDA and other clustering algorithms, to evaluate the performance based on different metrics. Finally, LAMDA-RD is validated in a real case study related to the identification of production states in a gas-lift well, with data stream. The results have shown that LAMDA-RD achieves a competitive performance with respect to the other well-known algorithms, especially in unbalanced benchmarks and benchmarks with an overlapping of around 9%. In these cases, our algorithm is the best, reaching a Rand Index (RI) >98%. Besides, it is consistently among the best for all metrics considered (Silhouette coefficient, modification of the Silhouette coefficient, WB-index, Performance Coefficient, among others) in all case studies analyzed in this paper. Finally, in the real case study, it is better in all the metrics.Ítem Detección de fraude en reclamaciones de hogar bajo un enfoque de aprendizaje no supervisado : un caso de estudio(Universidad EAFIT, 2022) Acevedo Maya, Sergio; Almonacid Hurtado, Paula MaríaÍtem An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data(SPRINGER, 2019-10-01) Ariza-Jiménez L.; Pinel N.; Villa L.F.; Quintero O.L.; Universidad EAFIT. Departamento de Ciencias; Ciencias Biológicas y Bioprocesos (CIBIOP)Unsupervised learning methods are commonly used to perform the non-trivial task of uncovering structure in biological data. However, conventional approaches rely on methods that make assumptions about data distribution and reduce the dimensionality of the input data. Here we propose the incorporation of entropy related measures into the process of constructing graph-based representations for biological datasets in order to uncover their inner structure. Experimental results demonstrated the potential of the proposed entropy-based graph data representation to cope with biological applications related to unsupervised learning problems, such as metagenomic binning and neuronal spike sorting, in which it is necessary to organize data into unknown and meaningful groups. © 2020, Springer Nature Switzerland AG.Ítem An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data(SPRINGER, 2019-10-01) Ariza-Jiménez L.; Pinel N.; Villa L.F.; Quintero O.L.; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoUnsupervised learning methods are commonly used to perform the non-trivial task of uncovering structure in biological data. However, conventional approaches rely on methods that make assumptions about data distribution and reduce the dimensionality of the input data. Here we propose the incorporation of entropy related measures into the process of constructing graph-based representations for biological datasets in order to uncover their inner structure. Experimental results demonstrated the potential of the proposed entropy-based graph data representation to cope with biological applications related to unsupervised learning problems, such as metagenomic binning and neuronal spike sorting, in which it is necessary to organize data into unknown and meaningful groups. © 2020, Springer Nature Switzerland AG.Ítem Entropy-based graph construction methods for unsupervised data structure detection(Universidad EAFIT, 2021) Ariza Jiménez, Leandro Fabio; Quintero Montoya, Olga Lucía; Pinel Peláez, NicolásÍtem Estimación de precio de oferta para una planta hidroeléctrica de baja regulación en la bolsa de energía(Universidad EAFIT, 2021) Mosquera Galvis, Liceth Cristina; Quintero Montoya,Olga Lucia