Now showing items 1-10 of 9189
Combining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units
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 ...
General types of spherical mean operators and k-functionals of fractional orders
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.
Kernel Penalized K-means: A feature selection method based on Kernel K-means
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 ...
QK-Means: A clustering technique based on community detection and K-Means for deployment of cluster head nodes
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 ...
Soft clustering - fuzzy and rough approaches and their extensions and derivatives
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 ...
Monitoring of structural integrity using unsupervised data clustering techniques
This work presents a comparative study of three unsupervised data clustering techniques used to perform the monitoring of the structural integrity of an agricultural tractor. The techniques used in this study are: K-Means, ...
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
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 ...
Land use image classification through optimum-path forest clustering
Land use classification has been paramount in the last years, since we can identify illegal land use and also to monitor deforesting areas. Although one can find several research works in the literature that address this ...