Examinando por Materia "K-means"
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Ítem A Multivariate Outlier Detection Methodology Based on S-Orthogonal DOBIN Projections(Universidad EAFIT, 2024) Cano Campiño, Andrés Mauricio; Ortiz Arias, SantiagoÍtem Análisis de la utilidad potencial del mercado colombiano a través de modelos de segmentación y customer life value para una empresa originadora de créditos de libranza(Universidad EAFIT, 2022) González Cano, Juan José; Montoya Cano, Jorge Esteban; Ochoa, NataliaCurrently companies define their target market to have a greater focus on certain individuals and groups of the population, however, they fail to understand in depth what is the future economic benefit that these market niches represent, to understand if their business model is attractive from a financial point of view. This project is directly focused on the Colombian financial sector, seeking to make a direct contribution to the way in which companies in this sector analyze and define the economic potential of their target market, through the use of analytical and financial tools such as segmentation models and Customer Life Value analysis, resulting in the value that each niche can possibly represent in utility for the company, allowing it to outline a business strategy that ensures sustainability over time and in the market. Thanks to the comprehensive capabilities of the project team, segmentation techniques will be used to support different types of variables to find very homogeneous groups in their individuals, but very heterogeneous among them and thus get to know which clusters will lead the company to obtain a greater benefit.Ítem Aplicación de técnicas de clusterización para la clasificación de música dance electrónica(Universidad EAFIT, 2023) Murillo Martínez, Carlos Alberto; Alunno, Marco; Martínez Vargas, Juan DavidAudio processing is one of the essential tasks for a data scientist, and audio analysis has applications in a diverse range of fields, such as medicine, telecommunications, improving sound quality in music production, and even military applications (filtering suspicious or terrorist audio). This project aims to use hard clustering techniques (such as k-means or k-nearest neighbor) and soft clustering techniques (such as fuzzy clustering) to classify input songs using different metrics. The classification methods will be used to segment previously processed input audios and obtain a sample of representative segments of the songs, determining their similarity with other songs of the same genre. Another technique that has proven effective for audio classification is convolutional neural networks (CNNs), which have been used in a wide range of fields. In the music field, they have been used to classify violin bowing techniques [1] and even detect potential heart problems using heartbeat sounds [2]. In this project, we will use this technique up to the point of feature extraction, and then use classical classification techniques to determine which group a section of a song belongs to.Ítem Demand forecasting in a manufacturing company, comparing traditional statistical and artificial intelligence models(Universidad EAFIT, 2022) Vásquez Jaramillo, Daniela; Castro Zuluaga, Carlos Alberto; Almonacid Hurtado, Paula MaríaÍtem Information retrieval on documents methodology based on entropy filtering methodologies(Inderscience Enterprises Ltd., 2015-01-01) Montoya, O.L.Q.; Villa, L.F.; Muñoz, S.; Arenas, A.C.R.; Bastidas, M.; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoInformation retrieval problem occurs when the target information is not available 'literally' into the set of documents. In problems in which the goal is to find 'hidden' information, it is important to develop hybrid methodologies or improve and design a new one. In this work the authors are dealing with identifying the most informative piece of data on a collection of documents, in order to obtain the best result on a posterior fuzzy clustering stage. The aim is to find similarities between the documents and a reference target, to establish relationships related to a non-literal feature. We propose to apply the well-known entropy term weighting scheme and then show a posterior different procedures to the right election of the interest data. This procedure brings the biggest amount of information within the smallest amount of data. Applying a specific selection procedure for a group of words, gives more information to differentiate and separate the documents after using the entropy weighting. This returns considerable results on the processing time and the right fuzzy clustering of the documents collection. Copyright © 2015 Inderscience Enterprises Ltd.