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A Direct Data-Cluster Analysis Method Based on Neutrosophic Set Implication

Sudan Jha1, Gyanendra Prasad Joshi2, Lewis Nkenyereya3, Dae Wan Kim4, *, Florentin Smarandache5

1 School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, 144411, India.
2 Department of Computer Science and Engineering, Sejong University, Seoul, 05006, Korea.
3 Department of Computer and Information Security, Sejong University, Seoul, 05006, Korea.
4 Department of Business Administration, Yeungnam University, Gyeongsan, 38541, Korea.
5 University of New Mexico, New Mexico, 87301, USA.

* Corresponding Author: Dae Wan Kim. Email: .

Computers, Materials & Continua 2020, 65(2), 1203-1220.


Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters. A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets. This paper focuses on cluster analysis based on neutrosophic set implication, i.e., a k-means algorithm with a threshold-based clustering technique. This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm. To evaluate the validity of the proposed method, several validity measures and validity indices are applied to the Iris dataset (from the University of California, Irvine, Machine Learning Repository) along with k-means and threshold-based clustering algorithms. The proposed method results in more segregated datasets with compacted clusters, thus achieving higher validity indices. The method also eliminates the limitations of threshold-based clustering algorithm and validates measures and respective indices along with k-means and thresholdbased clustering algorithms.


Cite This Article

S. Jha, G. Prasad Joshi, L. Nkenyereya, D. Wan Kim and F. Smarandache, "A direct data-cluster analysis method based on neutrosophic set implication," Computers, Materials & Continua, vol. 65, no.2, pp. 1203–1220, 2020.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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