Search results for " mining"

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…

Growing Hierarchical Self-organizing Maps and Statistical Distribution Models for Online Detection of Web Attacks

2013

In modern networks, HTTP clients communicate with web servers using request messages. By manipulating these messages attackers can collect confidential information from servers or even corrupt them. In this study, the approach based on anomaly detection is considered to find such attacks. For HTTP queries, feature matrices are obtained by applying an n-gram model, and, by learning on the basis of these matrices, growing hierarchical self-organizing maps are constructed. For HTTP headers, we employ statistical distribution models based on the lengths of header values and relative frequency of symbols. New requests received by the web-server are classified by using the maps and models obtaine…

Web mining based on Growing Hierarchical Self-Organizing Maps: Analysis of a real citizen web portal☆

2008

This work is focused on the usage analysis of a citizen web portal, Infoville XXI (http://www.infoville.es) by means of Self-Organizing Maps (SOM). In this paper, a variant of the classical SOM has been used, the so-called Growing Hierarchical SOM (GHSOM). The GHSOM is able to find an optimal architecture of the SOM in a few iterations. There are also other variants which allow to find an optimal architecture, but they tend to need a long time for training, especially in the case of complex data sets. Another relevant contribution of the paper is the new visualization of the patterns in the hierarchical structure. Results show that GHSOM is a powerful and versatile tool to extract relevant …

Semantic annotation and big data techniques for patent information processing

2017

This thesis analyzes approaches to generate semantic annotations on patent records, as well as on other structured data, by relying on the structure and semantic representation of documents. Information in patent records reflects how real-world technologies evolve, and the approximately 3 million annual new patent applications capture the global inventive frontier. The volume of this information is too big to be effectively analyzed purely with human effort, necessitating Big data approaches to analyze it with computer aided tools and techniques. Big data is a term that describes a massive volume of structured, semi structured and unstructured data that is so large to the point that it is d…

TRIF turns the switch for DIC in sepsis

2020

Healthcare trajectory mining by combining multidimensional component and itemsets

2012

Sequential pattern mining is aimed at extracting correlations among temporal data. Many different methods were proposed to either enumerate sequences of set valued data (i.e., itemsets) or sequences containing multidimensional items. However, in real-world scenarios, data sequences are described as events of both multidimensional items and set valued information. These rich heterogeneous descriptions cannot be exploited by traditional approaches. For example, in healthcare domain, hospitalizations are defined as sequences of multi-dimensional attributes (e.g. Hospital or Diagnosis) associated with two sets, set of medical procedures (e.g. $ \lbrace $ Radiography, Appendectomy $\rbrace$) and…

Occlusion-based estimation of independent multinomial random variables using occurrence and sequential information

2017

Abstract This paper deals with the relatively new field of sequence-based estimation in which the goal is to estimate the parameters of a distribution by utilizing both the information in the observations and in their sequence of appearance. Traditionally, the Maximum Likelihood (ML) and Bayesian estimation paradigms work within the model that the data, from which the parameters are to be estimated, is known, and that it is treated as a set rather than as a sequence. The position that we take is that these methods ignore, and thus discard, valuable sequence -based information, and our intention is to obtain ML estimates by “extracting” the information contained in the observations when perc…

A weighted logistic regression for conjoint analysis and Kansei engineering

2007

Customer needs for emotional satisfaction are increasingly being considered by product and service designers. While several existing methods such as conjoint analysis (CA), Kano model and quality function deployment support the translation of customer requirements into technical specifications, researchers are now working to develop methods aimed at integrating affective aspects into product design. Kansei engineering (KE) is a design philosophy that considers customer perceptions and emotions by adopting a multi-disciplinary approach. CA is a useful tool within a KE project. This article presents a methodology for conducting a KE project in early development phases. This methodology is bas…