Sudan Jha1, Gyanendra Prasad Joshi2, Lewis Nkenyereya3, Dae Wan Kim4, *, Florentin Smarandache5
CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1203-1220, 2020, DOI:10.32604/cmc.2020.011618
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 More >
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