The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
An Online Software for Decision Tree Classification and Visualization using C4.5 Algorithm (ODTC)
Classification is an important and widely carried out task of data mining. It is a predictive modelling task which is defined as building a model for the target variable as a function of the explanatory variables. There are many well established techniques for classification, while decision tree is a very important and popular technique from the machine learning domain. Decision tree is a decision_support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs and utility. C4.5 is a well known decision tree algorithm used for classifying datasets. The C4.5 algorithm is Quinlan's extension of his own ID3 algorithm for decision tree classification. It induces decision trees and generates rules from datasets, which could contain categorical and/or numerical attributes. The rules could he used to predict categorical values of attributes from new records. C4.5 performs well in classifying the dataset as well as in generating useful rules. In this paper, a web based software for rule generation and decision tree induction using C4.5 algorithm has been discussed. The visualization in the form of tree structure enhances the understanding of the generated rules. The software contains the feature to impute the missing values in data. The input data can both he categorical and numerical in nature. The software can import TXT, XLS and CSV data file formats. Enhanced waterfall model has been used for the software development process. This software will he useful for academicians, researchers and students working in the area of data mining, agriculture and other fields where huge amount of data is generated.
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