Mathematics and Statistics Faculty and Staff Publications

Document Type

Article

Publication Date

2020

Abstract

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 threshold based clustering algorithms.

Publisher

www.techscience.com

Publication Title

Computers, Materials & Continua

Volume

65

Issue

2

First Page

1203

Last Page

1220

DOI

doi:10.32604/cmc.2020.011618

Language (ISO)

English

Keywords

Data clustering, data mining, neutrosophic set, k-means, validity measures, cluster-based classification, hierarchical clustering

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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