The Profession of IT: The core of the third-wave professional
Communications of the ACM
Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Network Training Using Genetic Algorithms
Neural Network Training Using Genetic Algorithms
Data Mining in Time Series Database
Data Mining in Time Series Database
Discovering Knowledge in Data: An Introduction to Data Mining
Discovering Knowledge in Data: An Introduction to Data Mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Composite Systems Decisions (Decision Engineering)
Composite Systems Decisions (Decision Engineering)
A new approach to testing an integrated water systems model using qualitative scenarios
Environmental Modelling & Software
Editorial: Robotics and Autonomous Systems in the 50th Anniversary of Artificial Intelligence
Robotics and Autonomous Systems
Agent Technology and e-Health (Whitestein Series in Software Agent Technologies and Autonomic Computing)
Clustering based on matrix approximation: a unifying view
Knowledge and Information Systems
Embedding intelligent decision making within complex dynamic environments
Artificial Intelligence Review
Letters: Energy demand prediction using GMDH networks
Neurocomputing
Pattern-based time-series subsequence clustering using radial distribution functions
Knowledge and Information Systems
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques
A comprehensive survey of numeric and symbolic outlier mining techniques
Intelligent Data Analysis
Principles and Theory for Data Mining and Machine Learning
Principles and Theory for Data Mining and Machine Learning
International Journal of Intelligent Systems
Mining fuzzy association rules from uncertain data
Knowledge and Information Systems
Approximate data instance matching: a survey
Knowledge and Information Systems
Architecturing large integrated complex information systems: an application to healthcare
Knowledge and Information Systems
A plan classifier based on Chi-square distribution tests
Intelligent Data Analysis
Layering social interaction scenarios on environmental simulation
MABS'04 Proceedings of the 2004 international conference on Multi-Agent and Multi-Agent-Based Simulation
A fuzzy-neural approach for global CO2 concentration forecasting
Intelligent Data Analysis
Mapping atmospheric pollutants emissions in European countries
Intelligent Data Analysis
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Complex emergent systems are known to be ill-managed because of their complex nature. This article introduces a novel interdisciplinary approach towards their study. In this sense, the DeciMaS methodological approach to mining and simulating data in complex information systems is introduced. The DeciMaS framework consists of three principal phases, preliminary domain and system analysis, system design and coding, and simulation and decision making. The framework offers a sequence of steps in order to support a domain expert who is not a specialist in data mining during the knowledge discovery process. With this aim a generalized structure of a decision support system DSS has been worked out. The DSS is virtually and logically organized into a three-leveled architecture. The first layer is dedicated to data retrieval, fusion and pre-processing, the second one discovers knowledge from data, and the third layer deals with making decisions and generating output information. Data mining is aimed to solve the following problems: association, classification, function approximation, and clustering. DeciMaS populates the second logical level of the DSS with agents which are aimed to complete these tasks. The agents use a wide range of data mining procedures that include approaches for estimation and prediction: regression analysis, artificial networks ANNs, self-organizational methods, in particular, Group Method of Data Handling, and hybrid methods. The association task is solved with artificial neural networks. The ANNs are trained with different training algorithms such as backpropagation, resilient propagation and genetic algorithms. In order to assess the proposal an exhaustive experiment, designed to evaluate the possible harm caused by environmental contamination upon public health, is introduced in detail.