Neutrosophic K-means Based Method for Handling Unlabeled Data

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...

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Main Authors: Ned Vito Quevedo Arnaiz, Nemis Garcia Arias, Leny Cecilia Campaña Muñoz
Format: Article
Language:English
Published: University of New Mexico 2020-10-01
Series:Neutrosophic Sets and Systems
Subjects:
Online Access:https://fs.unm.edu/NSS/37NeutrosophicK-meansBased.pdf
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author Ned Vito Quevedo Arnaiz
Nemis Garcia Arias
Leny Cecilia Campaña Muñoz
author_facet Ned Vito Quevedo Arnaiz
Nemis Garcia Arias
Leny Cecilia Campaña Muñoz
author_sort Ned Vito Quevedo Arnaiz
collection DOAJ
description 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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spelling doaj.art-293b924ae6fa43febd9f23e0dd8cb3212023-08-04T15:17:41ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2020-10-013730831510.5281/zenodo.4122364Neutrosophic K-means Based Method for Handling Unlabeled DataNed Vito Quevedo Arnaiz Nemis Garcia AriasLeny Cecilia Campaña MuñozNowadays, 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.https://fs.unm.edu/NSS/37NeutrosophicK-meansBased.pdfmachine learningdata miningrough neutrosophic setsentropy
spellingShingle Ned Vito Quevedo Arnaiz
Nemis Garcia Arias
Leny Cecilia Campaña Muñoz
Neutrosophic K-means Based Method for Handling Unlabeled Data
Neutrosophic Sets and Systems
machine learning
data mining
rough neutrosophic sets
entropy
title Neutrosophic K-means Based Method for Handling Unlabeled Data
title_full Neutrosophic K-means Based Method for Handling Unlabeled Data
title_fullStr Neutrosophic K-means Based Method for Handling Unlabeled Data
title_full_unstemmed Neutrosophic K-means Based Method for Handling Unlabeled Data
title_short Neutrosophic K-means Based Method for Handling Unlabeled Data
title_sort neutrosophic k means based method for handling unlabeled data
topic machine learning
data mining
rough neutrosophic sets
entropy
url https://fs.unm.edu/NSS/37NeutrosophicK-meansBased.pdf
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AT lenyceciliacampanamunoz neutrosophickmeansbasedmethodforhandlingunlabeleddata