Search results for "Data mining"
showing 10 items of 907 documents
Neural networks for animal science applications: Two case studies
2006
Abstract Artificial neural networks have shown to be a powerful tool for system modelling in a wide range of applications. In this paper, we focus on neural network applications to intelligent data analysis in the field of animal science. Two classical applications of neural networks are proposed: time series prediction and clustering. The first task is related to the prediction of weekly milk production in goat flocks, which includes a knowledge discovery stage in order to analyse the relative relevance of the different variables. The second task is the clustering of goat flocks; it is used to analyse different livestock surveys by using self-organizing maps and the adaptive resonance theo…
Using SOM and PCA for analysing and interpreting data from a P-removal SBR
2008
This paper focuses on the application of Kohonen self-organizing maps (SOM) and principal component analysis (PCA) to thoroughly analyse and interpret multidimensional data from a biological process. The process is aimed at enhanced biological phosphorus removal (EBPR) from wastewater. In this work, SOM and PCA are firstly applied to the data set in order to identify and analyse the relationships among the variables in the process. Afterwards, K-means algorithm is used to find out how the observations can be grouped, on the basis of their similarity, in different classes. Finally, the information obtained using these intelligent tools is used for process interpretation and diagnosis. In the…
The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration
2014
Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a …
Automatic Detection of Hemangioma through a Cascade of Self-organizing Map Clustering and Morphological Operators
2016
Abstract In this paper we propose a method for the automatic detection of hemangioma regions, consisting of a cascade of algorithms: a Self Organizing Map (SOM) for clustering the image pixels in 25 classes (using a 5x5 output layer) followed by a morphological method of reducing the number of classes (MMRNC) to only two classes: hemangioma and non-hemangioma. We named this method SOM-MMRNC. To evaluate the performance of the proposed method we have used Fuzzy C-means (FCM) for comparison. The algorithms were tested on 33 images; for most images, the proposed method and FCM obtain similar overall scores, within one percent of each other. However, in about 18% of the cases, there is a signif…
Complexity Selection of the Self-Organizing Map
2002
This paper describes how the complexity of the Self-Organizing Map can be selected using the Minimum Message Length principle. The use of the method in textual data analysis is also demonstrated.
Studying the feasibility of a recommender in a citizen web portal based on user modeling and clustering algorithms
2006
This paper presents a methodology to estimate the future success of a collaborative recommender in a citizen web portal. This methodology consists of four stages, three of them are developed in this study. First of all, a user model, which takes into account some usual characteristics of web data, is developed to produce artificial data sets. These data sets are used to carry out a clustering algorithm comparison in the second stage of our approach. This comparison provides information about the suitability of each algorithm in different scenarios. The benchmarked clustering algorithms are the ones that are most commonly used in the literature: c-Means, Fuzzy c-Means, a set of hierarchical …
Visual Data Mining in Physiotherapy Using Self-Organizing Maps
2013
The basis of all clinical science developments is the analysis of the data obtained from a particular problem. In recent decades, however, the capacity of computers to process data has been increasing exponentially, which has created the possibility of applying more powerful methods of data analysis. Among these methods, the multidimensional visual data mining methods are outstanding. These methods show all the variables of one particular problem on the whole allowing to the clinical specialist to extract his own conclusions. In this chapter, a neural approximation to this kind of data mining is shown by means of the valuation analysis of the knee in athletes in the pre- and post-surgery of…
Self-organizing maps could improve the classification of Spanish mutual funds
2006
In this paper, we apply nonlinear techniques (Self-Organizing Maps, k-nearest neighbors and the k-means algorithm) to evaluate the official Spanish mutual funds classification. The methodology that we propose allows us to identify which mutual funds are misclassified in the sense that they have historical performances which do not conform to the investment objectives established in their official category. According to this, we conclude that, on average, over 40% of mutual funds could be misclassified. Then, we propose an alternative classification, based on a double-step methodology, and we find that it achieves a significantly lower rate of misclassifications. The portfolios obtained from…
Forecasting daily urban electric load profiles using artificial neural networks
2004
The paper illustrates a combined approach based on unsupervised and supervised neural networks for the electric energy demand forecasting of a suburban area with a prediction time of 24 h. A preventive classification of the historical load data is performed during the unsupervised stage by means of a Kohonen's self organizing map (SOM). The actual forecast is obtained using a two layered feed forward neural network, trained with the back propagation with momentum learning algorithm. In order to investigate the influence of climate variability on the electricity consumption, the neural network is trained using weather data (temperature, relative humidity, global solar radiation) along with h…
Visual Data Mining With Self-organizing Maps for “Self-monitoring” Data Analysis
2016
Data collected in psychological studies are mainly characterized by containing a large number of variables (multidimensional data sets). Analyzing multidimensional data can be a difficult task, especially if only classical approaches are used (hypothesis tests, analyses of variance, linear models, etc.). Regarding multidimensional models, visual techniques play an important role because they can show the relationships among variables in a data set. Parallel coordinates and Chernoff faces are good examples of this. This article presents self-organizing maps (SOM), a multivariate visual data mining technique used to provide global visualizations of all the data. This technique is presented as…