@Article{cmc.2020.011618,
AUTHOR = {Sudan Jha, Gyanendra Prasad Joshi, Lewis Nkenyereya, Dae Wan Kim, Florentin Smarandache},
TITLE = {A Direct Data-Cluster Analysis Method Based on Neutrosophic  Set Implication},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1203--1220},
URL = {http://www.techscience.com/cmc/v65n2/39869},
ISSN = {1546-2226},
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 <i>k</i>-means algorithm with a threshold-based 
clustering technique. This algorithm addresses the shortcomings of the <i>k</i>-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 <i>k</i>-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.},
DOI = {10.32604/cmc.2020.011618}
}