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
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New Trends in Neutrosophic Theory and Applications
Author: Florentin Smarandache (editor)
language: en
Publisher: Infinite Study
Release Date: 2016-11-05
Neutrosophic theory and applications have been expanding in all directions at an astonishing rate especially after the introduction the journal entitled “Neutrosophic Sets and Systems”. New theories, techniques, algorithms have been rapidly developed. One of the most striking trends in the neutrosophic theory is the hybridization of neutrosophic set with other potential sets such as rough set, bipolar set, soft set, hesitant fuzzy set, etc. The different hybrid structure such as rough neutrosophic set, single valued neutrosophic rough set, bipolar neutrosophic set, single valued neutrosophic hesitant fuzzy set, etc. are proposed in the literature in a short period of time. Neutrosophic set has been a very important tool in all various areas of data mining, decision making, e-learning, engineering, medicine, social science, and some more. The book “New Trends in Neutrosophic Theories and Applications” focuses on theories, methods, algorithms for decision making and also applications involving neutrosophic information. Some topics deal with data mining, decision making, e-learning, graph theory, medical diagnosis, probability theory, topology, and some more. 30 papers by 39 authors and coauthors.
Role of Neutrosophic Logic in Data Mining
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.
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.
In this paper, we define two new type of operators of fuzzy matrices denoted by the symbol ⊕ and . ⊗ Using these operators of fuzzy matrices we define row-maxaverage norm, column-max-average norm. Here instead of addition of fuzzy matrices we use the operator ⊕ and instead of multiplication of fuzzy matrices we use the operator . ⊗ We also define Pseudo norm of fuzzy matrices and max-min norm.