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Mostrando ítems 1-10 de 39
Artículo
Selecting the best measures to discover quantitative association rules
(Elsevier, 2014)
The majority of the existing techniques to mine association rules typically use the support and the confidence to evaluate the quality of the rules obtained. However, these two measures may not be sufficient to properly ...
Artículo
Obtaining optimal quality measures for quantitative association rules
(Elsevier, 2016)
There exist several works in the literature in which fitness functions based on a combination of weighted measures for the discovery of association rules have been proposed. Nevertheless, some differences in the measures ...
Tesis Doctoral
Técnicas avanzadas de predicción y optimización aplicadas a sistemas de potencia
(2004-07)
Esta tesis está enmarcada básicamente dentro de dos campos de investigación, la minería de datos y la optimización. Principalmente tiene un carácter aplicado, ya que se han investigado tanto técnicas de predicción como de ...
Artículo
Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities
(MDPI, 2018)
New technologies such as sensor networks have been incorporated into the management of buildings for organizations and cities. Sensor networks have led to an exponential increase in the volume of data available in recent ...
Capítulo de Libro
Partitioning-Clustering Techniques Applied to the Electricity Price Time Series
(2007)
Clustering is used to generate groupings of data from a large dataset, with the intention of representing the behavior of a system as accurately as possible. In this sense, clustering is applied in this work to extract ...
Capítulo de Libro
Quantitative Association Rules Applied to Climatological Time Series Forecasting
(2009)
This work presents the discovering of association rules based on evolutionary techniques in order to obtain relationships among correlated time series. For this purpose, a genetic algorithm has been proposed to determine ...
Artículo
Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets
(iOS Press, 2015)
Association rule mining is a well-known methodology to discover significant and apparently hidden relations among attributes in a subspace of instances from datasets. Genetic algorithms have been extensively used to find ...
Capítulo de Libro
Time-Series Prediction: Application to the Short-Term Electric Energy Demand
(2003)
This paper describes a time-series prediction method based on the kNN technique. The proposed methodology is applied to the 24-hour load forecasting problem. Also, based on recorded data, an alternative model is developed ...
Artículo
Applications of Computational Intelligence in Time Series
(Hindawi, 2017)
Artículo
Energy Time Series Forecasting Based on Pattern Sequence Similarity
(IEEE, 2011)
This paper presents a new approach to forecast the behavior of time series based on similarity of pattern sequences. First, clustering techniques are used with the aim of grouping and labeling the samples from a data set. ...