Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Data preparation for data mining
Data preparation for data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Delivering Business Intelligence with Microsoft SQL Server 2005
Delivering Business Intelligence with Microsoft SQL Server 2005
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The vinification process is one important stages of the wines production that can influence the achievement of wines quality. Based in a chemical samples this assessment is traditionally realized by wine tasters that analyze some subjective parameters such as colour, foam, flavour and savour. This type of analysis is very important for the production of wine and for its successful marketing. The use of Data Mining techniques in this field has a great relevance in revealing the importance of the numerous chemical parameters involved in the process of wine production, as well as to define models to classify classes of parameters for example, to determine the organoleptic parameters based on chemical parameters of the winemaking process. This paper presents the Decision Trees, Artificial Neural Networks and Linear Regression as Data Mining techniques to achieve the objectives of classification and regression in order to create models to predict the organoleptic parameters from the chemical parameters of the vinification process. The experiments were oriented using the new Microsoft's SQL Server 2008 Business Intelligence Development and an open-source Data Mining tool (WEKA). Very good results were achieved with accuracies between 86% and 99% obtained for all models.