New Trends in Neutrosophic Theory and Applications

This paper presents a data mining process of single valued neutrosophic information. This approach gives a presentation of data analysis common to all applications. Data mining depends on two main elements, namely the concept of similarity and the machine learning framework. It describes a lot of real world applications for the domains namely mathematical, medical, educational, chemical, multimedia etc. There are two main types of indeterminacy in supervised learning: cognitive and statistical. Statistical indeterminacy deals with the random behavior of nature. All existing data mining techniques can handle the uncertainty that arises (or is assumed to arise) in the natural world from statistical variations or randomness. Cognitive uncertainty deals with human cognition. In real world problems for data mining, indeterminacy components may arise. Neutrosophic logic can handle this situation. In this paper, we have shown the role of single valued neutrosophic set logic in data mining. We also propose a data mining approach in single valued neutrosophic environment.