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Kernel Penalized K-means: A feature selection method based on Kernel K-means
(Elsevier, 2015)
We present an unsupervised method that selects the most relevant features using an embedded strategy while maintaining the cluster structure found with the initial feature set. It is based on the idea of simultaneously ...
Soft clustering - fuzzy and rough approaches and their extensions and derivatives
(Elsevier, 2013)
Clustering is one of the most widely used approaches in data mining with real life
applications in virtually any domain. The huge interest in clustering has led to a possibly
three-digit number of algorithms with the ...
Uma análise do algoritmo K-means como introdução ao Aprendizado de Máquinas.
(Universidade Federal do TocantinsAraguaínaCURSO::ARAGUAÍNA::PRESENCIAL::LICENCIATURA::MATEMÁTICAAraguaínaGraduação, 2023)
Identificación De Patrones De Trayectorias Vehiculares Usando El Algoritmo K-Means
(Universidad de Guayaquil.Facultad de Ciencias Matematicas y Fisicas.Carrera en Ingenieria en Sistemas Computacionales, 2017-07-20)
Esta investigación se centra en el estudio del algoritmo k-means como medio de agrupación de información con el objetivo de poder conocer su funcionamiento y procesamiento de información. De la información obtenida se ...
Identificação da passada de um exoesqueleto utilizando algoritmo de classificação não supervisionado k-means
(Universidade Estadual Paulista (Unesp), 2021-12-20)
O objetivo deste trabalho é estudar o comportamento cinemático de um modelo de exoesqueleto de membros inferiores, a fim de identificar padrões de passada utilizando métricas biomecânicas quantitativas. As ferramentas para ...
Efficiency issues of evolutionary k-means
(ELSEVIER SCIENCE BV, 2011)
One of the top ten most influential data mining algorithms, k-means, is known for being simple and scalable. However, it is sensitive to initialization of prototypes and requires that the number of clusters be specified ...
Evolutionary k-means for distributed data sets
(ElsevierAmsterdam, 2014-03-15)
One of the challenges for clustering resides in dealing with data distributed in separated repositories, because most clustering techniques require the data to be centralized. One of them, k-means, has been elected as one ...
Combining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units
(2016-04-01)
Data clustering is related to the split of a set of objects into smaller groups with common features. Several optimization techniques have been proposed to increase the performance of clustering algorithms. Swarm Intelligence ...
QK-Means: A clustering technique based on community detection and K-Means for deployment of cluster head nodes
(2012-08-22)
Wireless Sensor Networks (WSN) are a special kind of ad-hoc networks that is usually deployed in a monitoring field in order to detect some physical phenomenon. Due to the low dependability of individual nodes, small radio ...