Neutrosophic K-means Based Method for Handling Unlabeled Data

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.

Keywords

• machine learning
• data mining
• rough neutrosophic sets
• entropy