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

作     者：Sudan Jha Gyanendra Prasad Joshi Lewis Nkenyereya Dae Wan Kim Florentin Smarandache 

作者机构：School of Computer Science and EngineeringLovely Professional UniversityPhagwaraPunjab144411India Department of Computer Science and EngineeringSejong UniversitySeoul05006Korea Department of Computer and Information SecuritySejong UniversitySeoul05006Korea Department of Business AdministrationYeungnam UniversityGyeongsan38541Korea University of New MexicoNew Mexico87301USA 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2020年第65卷第11期

页      面：1203-1220页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Sejong University 

主　　题：Data clustering data mining neutrosophic set k-means validity measures cluster-based classification hierarchical clustering 

摘      要：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 *** paper focuses on cluster analysis based on neutrosophic set implication,i.e.,a k-means algorithm with a threshold-based clustering *** algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering *** 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 *** proposed method results in more segregated datasets with compacted clusters,thus achieving higher validity *** 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.