Buscar
Mostrando ítems 1-7 de 7
Ponencia
On the use of algorithms to discover motifs in DNA sequences
(IEEE, 2011)
Many approaches are currently devoted to find DNA motifs in nucleotide sequences. However, this task remains challenging for specialists nowadays due to the difficulties they find to deeply understand gene regulatory ...
Capítulo de Libro
Analysis of Measures of Quantitative Association Rules
(Springer, 2011)
This paper presents the analysis of relationships among different interestingness measures of quality of association rules as first step to select the best objectives in order to develop a multi-objective algorithm. For ...
Artículo
An evolutionary algorithm to discover quantitative association rules in multidimensional time series
(Springer, 2011)
An evolutionary approach for finding existing relationships among several variables of a multidimensional time series is presented in this work. The proposed model to discover these relationships is based on quantitative ...
Capítulo de Libro
Inferring Gene-Gene Associations from Quantitative Association Rules
(IEEE, 2011)
The microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The interest in this technique has grown exponentially in recent years and the difficulties in analyzing ...
Capítulo de Libro
Computational Intelligence Techniques for Predicting Earthquakes
(Springer, 2011)
Nowadays, much effort is being devoted to develop techniques that forecast natural disasters in order to take precautionary measures. In this paper, the extraction of quantitative association rules and regression ...
Artículo
Discovery of motifs to forecast outlier occurrence in time series
(Elsevier, 2011)
The forecasting process of real-world time series has to deal with especially unexpected values, commonly known as outliers. Outliers in time series can lead to unreliable modeling and poor forecasts. Therefore, the ...
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. ...