On Objective Measures of Rule Surprisingness

Most of the literature argues that surprisingness is an inherently subjective aspect of the discovered knowledge, which cannot be measured in objective terms. This paper departs from this view, and it has a twofold goal: (1) showing that it is indeed possible to define objective (rather than subjective) measures of discovered rule surprisingness; (2) proposing new ideas and methods for defining objective rule surprisingness measures

Forecasting the Accident Frequency and Risk Factors: A Case Study of Erzurum, Turkey

Nowadays, life is intimately associated with transportation, generating several issues on it. Numerous works are available concerning accident prediction techniques depending on independent road and traffic features, while the mix parameters including time, geometry, traffic flow, and weather conditions are still rarely ever taken into consideration. This study aims to predict future accident frequency and the risk factors of traffic accidents. It utilizes the Generalized Linear Model (GLM) and Artificial Neural Networks (ANN) approaches to process and predict traffic data efficiently based on 21500 records of traffic accidents that occurred in Erzurum in Turkey from 2005 to 2019. The results of the comparative evaluation demonstrated that the ANN model outperformed the GLM model. The study revealed that the most effective variable was the number of horizontal curves. The annual average growth rates of accident occurrences based on the ANNꞌs method are predicted to be 11.22% until 2030

A process for mining science & technology documents databases, illustrated for the case of "knowledge discovery and data mining"

This paper presents a process of mining research & development abstract databases to profile current status and to project potential developments for target technologies, The process is called “technology opportunities analysis.” This article steps through the process using a sample data set of abstracts from the INSPEC database on the topic o “knowledge discovery and data mining.” The paper offers a set of specific indicators suitable for mining such databases to understand innovation prospects. In illustrating the uses of such indicators, it offers some insights into the status of knowledge discovery research

USE OF THE ARTIFICIAL INTELLIGENCE FOR DRIVERS BEHAVIOR ANALYSIS AND VEHICULAR CRASHES GENERATION

This research is the result of the analysis of drivers´ behavior in a controlled scenario, using a driving simulator, in which, by measuring brain waves, the degree of concentration was (measured when driving and through the use of networks neuronal and artificial intelligence, a model of behavior of drivers was proposed to be subjected to a distracting effect while driving, which allows analyzing the most relevant factors that are reflected in errors and bad practices at the time of driving. In this research it was determined a population sample of men and woman whose ages oscillate between 16 to 90 years, from a universe obtained from a database of fatalities for 7 years. A driving simulator was built, and it was using a software for the simulation that allows different driving scenarios. Finally, risk behaviors were classified to be a factor of distraction. .&nbsp

QUERANDO!: un agente de filtrado de documentos web

La gran cantidad de información en la WWW requiere de métodos superiores de asistencia al usuario con respecto a la funcionalidad provista por los motores de búsqueda actuales. Enfoques recientes en el área de agentes inteligentes ayudan a los usuarios a solucionar este problema mediante la adquisición de perfiles de filtrado de documentos. El presente artículo describe un agente de filtrado de páginas web llamado Querando! capaz de aprender perfiles representativos de las preferencias del usuario, a través de una red neuronal basada en la Teoría de la Resonancia Adaptativa Difusa. El modelo utilizado en este agente se caracteriza por permitir el perfeccionamiento del perfil a través del tiempo sin requerir reentrenamiento con documentos previos. Finalmente, se discute la implementación de Querando! y se enumeran los resultados obtenidos que avalan dicho enfoque comparándolo con otras soluciones existentes.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

QUERANDO!: un agente de filtrado de documentos web

La gran cantidad de información en la WWW requiere de métodos superiores de asistencia al usuario con respecto a la funcionalidad provista por los motores de búsqueda actuales. Enfoques recientes en el área de agentes inteligentes ayudan a los usuarios a solucionar este problema mediante la adquisición de perfiles de filtrado de documentos. El presente artículo describe un agente de filtrado de páginas web llamado Querando! capaz de aprender perfiles representativos de las preferencias del usuario, a través de una red neuronal basada en la Teoría de la Resonancia Adaptativa Difusa. El modelo utilizado en este agente se caracteriza por permitir el perfeccionamiento del perfil a través del tiempo sin requerir reentrenamiento con documentos previos. Finalmente, se discute la implementación de Querando! y se enumeran los resultados obtenidos que avalan dicho enfoque comparándolo con otras soluciones existentes.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Data Mining: er der guld i virksomhedens data?

Data mining omhandler nyere metoder til analyse af store mængder af virksomhedens data. I denne oversigtsartikel omtales flere af de udbredte metoder anvendt indenfor data mining. Speciel opmærksomhed henledes på objektet for data mining: virksomhedens data skabt i virksomhedens processer. Da stadig flere processer frembringer stadig flere data, producerer virksomheden en stigende strøm af data; specielt udløser anvendelsen af Internet en eksplosion af datamængden. Analysen af de store datamængder kræver i sig selv et højt stade af informationsteknologi. Samtidig medfører behovet for frembringelse af velegnede data til data mining, at etablering af et data warehouse i virksomheden bliver et krav for med sikkerhed at kunne forsyne virksomhedens data mining med integrerede og valide data. Artiklen illustrerer, hvorledes data warehouse og data mining er elementer i virksomhedens aktive akkvisition af data/information/viden, aktive produktion og den aktive udbredelse af virksomhedens viden. Med anvendelse af data warehouse og data mining foretager virksomheden en bevægelse fra passiv opsamling og passiv udbredelse af viden – virksomheden som pulterrum for viden – til virksomheden som videnspumpe1

Using visualization, variable selection and feature extraction to learn from industrial data

Although the engineers of industry have access to process data, they seldom use advanced statistical tools to solve process control problems. Why this reluctance? I believe that the reason is in the history of the development of statistical tools, which were developed in the era of rigorous mathematical modelling, manual computation and small data sets. This created sophisticated tools. The engineers do not understand the requirements of these algorithms related, for example, to pre-processing of data. If algorithms are fed with unsuitable data, or parameterized poorly, they produce unreliable results, which may lead an engineer to turn down statistical analysis in general. This thesis looks for algorithms that probably do not impress the champions of statistics, but serve process engineers. This thesis advocates three properties in an algorithm: supervised operation, robustness and understandability. Supervised operation allows and requires the user to explicate the goal of the analysis, which allows the algorithm to discover results that are relevant to the user. Robust algorithms allow engineers to analyse raw process data collected from the automation system of the plant. The third aspect is understandability: the user must understand how to parameterize the model, what is the principle of the algorithm, and know how to interpret the results. The above criteria are justified with the theories of human learning. The basis is the theory of constructivism, which defines learning as construction of mental models. Then I discuss the theories of organisational learning, which show how mental models influence the behaviour of groups of persons. The next level discusses statistical methodologies of data analysis, and binds them to the theories of organisational learning. The last level discusses individual statistical algorithms, and introduces the methodology and the algorithms proposed by this thesis. This methodology uses three types of algorithms: visualization, variable selection and feature extraction. The goal of the proposed methodology is to reliably and understandably provide the user with information that is related to a problem he has defined interesting. The above methodology is illustrated by an analysis of an industrial case: the concentrator of the Hitura mine. This case illustrates how to define the problem with off-line laboratory data, and how to search the on-line data for solutions. A major advantage of algorithmic study of data is efficiency: the manual approach reported in the early took approximately six man months; the automated approach of this thesis created comparable results in few weeks.reviewe