Fully Automated Density-Based Clustering Method

作     者：Bilal Bataineh Ahmad A.Alzahrani 

作者机构：Information Systems DepartmentCollege of Computers and Information SystemsMakkahSaudi Arabia 

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

年 卷 期：2023年第76卷第8期

页      面：1833-1851页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the Deanship of Scientific Research at Umm Al-Qura University Grant Code:(23UQU4361009DSR001) 

主　　题：Automated clustering data mining density-based clustering unsupervised machine learning 

摘      要：Cluster analysis is a crucial technique in unsupervised machine learning,pattern recognition,and data ***,current clustering algorithms suffer from the need for manual determination of parameter values,low accuracy,and inconsistent performance concerning data size and *** address these challenges,a novel clustering algorithm called the fully automated density-based clustering method(FADBC)is *** FADBC method consists of two stages:parameter selection and cluster *** the first stage,a proposed method extracts optimal parameters for the dataset,including the epsilon size and a minimum number of points *** parameters are then used in a density-based technique to scan each point in the dataset and evaluate neighborhood densities to find *** proposed method was evaluated on different benchmark datasets andmetrics,and the experimental results demonstrate its competitive performance without requiring manual *** results show that the FADBC method outperforms well-known clustering methods such as the agglomerative hierarchical method,k-means,spectral clustering,DBSCAN,FCDCSD,Gaussian mixtures,and density-based spatial clustering *** can handle any kind of data set well and perform excellently.