A Direct Data-Cluster Analysis Method Based on Neutrosophic Set Implication |
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Authors: | Sudan Jha Gyanendra Prasad Joshi Lewis Nkenyereya Dae Wan Kim Florentin Smarandache |
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Affiliation: | 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. |
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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 thresholdbased clustering algorithms. |
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Keywords: | k-means validity measures cluster-based classification hierarchical clustering Data clustering" target="_blank">" target="_blank">Data clustering data mining neutrosophic set k-means validity measures cluster-based classification hierarchical clustering |
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