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A GH-SOM optimization with SOM labelling and dunn index
(Scopus, 2011)
Clustering is an unsupervised classification method that divides a data set in groups, where the elements of a group have similar characteristics to each other. A well-known clustering method is the Growing Hierarchical ...
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 ...
Clustering via ant colonies: Parameter analysis and improvement of the algorithm
(2020-04-18)
An ant colony optimization approach for partitioning a set of objects is proposed. In order to minimize the intra-variance, or within sum-of-squares, of the partitioned classes, we construct ant-like solutions by a ...
Au13-nAgn clusters: a remarkably simple trend
(Royal Soc Chemistry, 2015)
The planar to three dimensional transition of Au13-nAgn clusters is investigated. To do so the low
lying energy configurations for all possible concentrations (n values) are evaluated. Many
thousands of possible conformations ...
Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems
(Institute of Electrical and Electronics Engineers (IEEE), 2006-07-01)
One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally ...
Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems
(Institute of Electrical and Electronics Engineers (IEEE), 2006-07-01)
One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally ...
Enhancing Brain Storm Optimization Through Optimum-Path Forest
(Ieee, 2018-01-01)
Among the many interesting meta-heuristic optimization algorithms, one can find those inspired by both the swarm and social behavior of human beings. The Brain Storm Optimization (BSO) is motivated by the brainstorming ...
Using metaheuristics to optimize the combination of classifier and cluster ensembles
(IOS PressAmsterdam, 2015)
We investigate how to make a simpler version of an existing algorithm, named 'C POT. 3'E, from Consensus between Classification and Clustering Ensembles, more user-friendly by automatically tuning its main parameters with ...
VDBSCAN plus : Performance Optimization Based on GPU Parallelism
(Ieee, 2013-01-01)
Spatial data mining techniques enable the knowledge extraction from spatial databases. However, the high computational cost and the complexity of algorithms are some of the main problems in this area. This work proposes a ...
Automatic aspect discrimination in data clustering
(ELSEVIER SCI LTDOXFORD, 2012)
The attributes describing a data set may often be arranged in meaningful subsets, each of which corresponds to a different aspect of the data. An unsupervised algorithm (SCAD) that simultaneously performs fuzzy clustering ...