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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 ...
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 ...
General types of spherical mean operators and k-functionals of fractional orders
(AIMSSpringfield, 2015-05)
We design a general type of spherical mean operators and employ them to approximate 'L IND.P' class functions. We show that optimal orders of approximation are achieved via appropriately defined K-functionals of fractional orders.
Uma análise do algoritmo K-means como introdução ao Aprendizado de Máquinas.
(Universidade Federal do TocantinsAraguaínaCURSO::ARAGUAÍNA::PRESENCIAL::LICENCIATURA::MATEMÁTICAAraguaínaGraduação, 2023)
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 ...
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 ...
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 ...
Classification of LiDAR data over building roofs using k-means and principal component analysisClassificação de dados LiDAR sobre telhados de edificações usando k-médias e análise de componentes principais
(2018-01-01)
The classification is an important step in the extraction of geometric primitives from LiDAR data. Normally, it is applied for the identification of points sampled on geometric primitives of interest. In the literature ...