Search results for "Data mining"
showing 10 items of 907 documents
An author keyword analysis for mapping Sport Sciences
2018
[EN] Scientific production has increased exponentially in recent years. It is necessary to find methodological strategies for understanding holistic or macro views of the major research trends developed in specific fields. Data mining is a useful technique to address this task. In particular, our study presents a global analysis of the information generated during last decades in the Sport Sciences Category (SSC) included in the Web of Science database. An analysis of the frequency of appearance and the dynamics of the Author Keywords (AKs) has been made for the last thirty years. Likewise, the network of co-occurrences established between words and the survival time of new words that have …
On the Implementation of HealthAgents: Agent-Based Brain Tumour Diagnosis
2007
This paper introduces HealthAgents, an EC-funded research project to improve the classification of brain tumours through multi-agent decision support over a secure and distributed network of local databases or Data Marts. HealthAgents will not only develop new pattern recognition methods for distributed classification and analysis of in vivo MRS and ex vivo/in vitro HRMAS and DNA data, but also define a method to assess the quality and usability of a new candidate local database containing a set of new cases, based on a compatibility score. Using its Multi-Agent architecture, HealthAgents intends to apply cutting-edge agent technology to the Biomedical field and develop the HealthAgents net…
A dynamic integration algorithm for an ensemble of classifiers
1999
Numerous data mining methods have recently been developed, and there is often a need to select the most appropriate data mining method or methods. The method selection can be done statically or dynamically. Dynamic selection takes into account characteristics of a new instance and usually results in higher classification accuracy. We discuss a dynamic integration algorithm for an ensemble of classifiers. Our algorithm is a new variation of the stacked generalization method and is based on the basic assumption that each basic classifier is best inside certain subareas of the application domain. The algorithm includes two main phases: a learning phase, which collects information about the qua…
An integrated information system for the acquisition, management and sharing of environmental data aimed to decision making
2012
This paper reports the first results of the Project SESAMO - SistEma informativo integrato per l’acquisizione, geStione e condivisione di dati AMbientali per il supportO alle decisioni (Integrated Information System for the acquisition, management and sharing of environmental data aimed to decision making). The main aim of the project is to design and develop an integrated environmental information platform able to provide monitoring services for decision support, integrating data from different environmental monitoring systems (including WSN). This ICT platform, based on a service-oriented architecture (SOA), will be developed to coordinate a wide variety of data acquisition systems, based…
A knowledge-based decision support system in bioinformatics: An application to protein complex extraction
2013
Abstract Background We introduce a Knowledge-based Decision Support System (KDSS) in order to face the Protein Complex Extraction issue. Using a Knowledge Base (KB) coding the expertise about the proposed scenario, our KDSS is able to suggest both strategies and tools, according to the features of input dataset. Our system provides a navigable workflow for the current experiment and furthermore it offers support in the configuration and running of every processing component of that workflow. This last feature makes our system a crossover between classical DSS and Workflow Management Systems. Results We briefly present the KDSS' architecture and basic concepts used in the design of the knowl…
Feature extraction for classification in knowledge discovery systems
2003
Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of "the curse of dimensionality". We consider three different eigenvector-based feature extraction approaches for classification. The summary of obtained results concerning the accuracy of classification schemes is presented and the issue of search for the most appropriate feature extraction method for a given data set is considered. A decision support system to aid in the integration of the feature extraction and classification processes is proposed. The goals and requirements set for the d…
Research on Application of Data Mining Methods to Diagnosing Gastric Cancer
2012
Constantly evolving technologies bring new possibilities for supporting decision making in different areas - finance, marketing, production, social area, healthcare and others. Decision support systems are widely used in medicine in developed countries and show positive results. This research reveals several possibilities of application of data mining methods to diagnosing gastric cancer, which is the fourth leading cancer type in incidence after the breast, lung and colorectal cancers. A simple decision support system model was introduced and tested using gastric cancer inquiry form statistical data. The obtained results reveal both the benefits and potential of application of DSS aimed to…
An Analysis of Earthquakes Clustering Based on a Second-Order Diagnostic Approach
2009
A diagnostic method for space–time point process is here introduced and applied to seismic data of a fixed area of Japan. Nonparametric methods are used to estimate the intensity function of a particular space–time point process and on the basis of the proposed diagnostic method, second-order features of data are analyzed: this approach seems to be useful to interpret space–time variations of the observed seismic activity and to focus on its clustering features.
Physics, Techniques and Review of Neuroradiological Applications of Diffusion Kurtosis Imaging (DKI)
2016
In recent years many papers about diagnostic applications of diffusion tensor imaging (DTI) have been published. This is because DTI allows to evaluate in vivo and in a non-invasive way the process of diffusion of water molecules in biological tissues. However, the simplified description of the diffusion process assumed in DTI does not permit to completely map the complex underlying cellular components and structures, which hinder and restrict the diffusion of water molecules. These limitations can be partially overcome by means of diffusion kurtosis imaging (DKI). The aim of this paper is the description of the theory of DKI, a new topic of growing interest in radiology. DKI is a higher or…
An Heuristic Approach for the Training Dataset Selection in Fingerprint Classification Tasks
2015
Fingerprint classification is a key issue in automatic fingerprint identification systems. It aims to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper an heuristic approach using only the directional image information for the training dataset selection in fingerprint classification tasks is described. The method combines a Fuzzy C-Means clustering method and a Naive Bayes Classifier and it is composed of three modules: the first module builds the working datasets, the second module extracts the training images dataset and, finally, the third module classifies fingerprint images in four classes. Unlike literature approaches using …