Neutrosophic K-means Based Method for Handling Unlabeled Data

Authors

  • Ned Vito Quevedo Arnaiz Universidad Regional Autónoma de los Andes (UNIANDES), Avenida La Lorena, CP 230150, Santo Domingo de los Tsáchilas, Ecuador
  • Nemis Garcia Arias Universidad Regional Autónoma de los Andes (UNIANDES), Avenida La Lorena, CP 230150, Santo Domingo de los Tsáchilas, Ecuador
  • Leny Cecilia Campaña Muñoz Universidad Regional Autónoma de los Andes (UNIANDES), Avenida La Lorena, CP 230150, Santo Domingo de los Tsáchilas, Ecuador

Keywords:

Machine learning, data mining, rough neutrosophic sets, entropy

Abstract

Nowadays, incalculable volumes of data are generated due to the technological development achieved by the current society of information. The exponential growth of information significantly supports people's decision making in their daily activities. In Ecuador, there are many institutions that store the data of their processes. The tourism sector represents an example of this assertion. However, the data generated exceeds the power of analysis and processing of human beings, sometimes relevant information is presented it is not visible for persons. The present investigation proposes a solution to the described problem starting from the development of a method for the treatment of unlabeled data. The proposed method is based on the unsupervised k-means algorithm. We used rough neutrosophic sets to reduce the number of attributes. The proposal has been implemented from the stored dataset of the tourism sector in the City of Riobamba.

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Published

2020-10-19

How to Cite

Quevedo Arnaiz, N. V. ., Garcia Arias, N. ., & Campaña Muñoz, L. C. . (2020). Neutrosophic K-means Based Method for Handling Unlabeled Data. Neutrosophic Sets and Systems, 37, 308-315. https://fs.unm.edu/nss8/index.php/111/article/view/873