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Automatic fuzzy-DBSCAN algorithm for morphological and overlapping datasets

查看全文 作　　者：YELGHI [1]Aref;KÖSE [2]Cemal;YELGHI [3]Asef;SHAHKAR [4]Amir 高影响力作者 机构地区：[1]Department of Computer Engineering,Avrasya University,Trabzon 61250,Turkey;[2]Department of Computer Engineering,Karadeniz Technical University,Trabzon 61080,Turkey;[3]Department of Business Administration,Gazi University,Ankara 06560,Turkey;[4]Civil Engineering Department,Karadeniz Technical University,Trabzon 61080,Turkey高影响力机构 出　　处：《Journal of Systems Engineering and Electronics》索引2020年第31卷第6期,共9页高影响力期刊 摘　　要：Clustering is one of the unsupervised learning problems.It is a procedure which partitions data objects into groups.Many algorithms could not overcome the problems of morphology,overlapping and the large number of clusters at the same time.Many scientific communities have used the clustering algorithm from the perspective of density,which is one of the best methods in clustering.This study proposes a density-based spatial clustering of applications with noise(DBSCAN)algorithm based on the selected high-density areas by automatic fuzzy-DBSCAN(AFD)which works with the initialization of two parameters.AFD,by using fuzzy and DBSCAN features,is modeled by the selection of high-density areas and generates two parameters for merging and separating automatically.The two generated parameters provide a state of sub-cluster rules in the Cartesian coordinate system for the dataset.The model overcomes the problems of clustering such as morphology,overlapping,and the number of clusters in a dataset simultaneously.In the experiments,all algorithms are performed on eight data sets with 30 times of running.Three of them are related to overlapping real datasets and the rest are morphologic and synthetic datasets.It is demonstrated that the AFD algorithm outperforms other recently developed clustering algorithms. 关 键 词：CLUSTERING density-based spatial clustering of applications with noise(DBSCAN) FUZZY OVERLAPPING data mining
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