Automatic Data Clustering Based Mean Best Artificial Bee Colony Algorithm

作     者：Ayat Alrosan Waleed Alomoush Mohammed Alswaitti Khalid Alissa Shahnorbanun Sahran Sharif Naser Makhadmeh Kamal Alieyan 

作者机构：Deanship of Information and Communication TechnologyImam Abdulrahman bin Faisal UniversityDammamSaudi Arabia Computer DepartmentImam Abdulrahman bin Faisal UniversityDammamSaudi Arabia School of Electrical and Computer EngineeringDepartment of Information and Communication TechnologyXiamen University MalaysiaSepang43900Malaysia Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityDammamSaudi Arabia Center for Artificial Intelligence Technology(CAIT)Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600 BangiMalaysia School of Computer SciencesUniversiti Sains Malaysia11800PenangMalaysia Faculty of Computer Sciences and InformaticsAmman Arab UniversityAmmanJordan 

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

年 卷 期：2021年第68卷第8期

页      面：1575-1593页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Research Management Center Xiamen University Malaysia under XMUM Research Program Cycle 4(Grant No:XMUMRF/2019-C4/IECE/0012) 

主　　题：Artificial bee colony automatic clustering natural images validity index number of clusters 

摘      要：Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine *** main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster *** Bee Colony(ABC)is a type of swarm algorithm that strives to improve the members’solution quality as an iterative process with the utilization of particular kinds of ***,ABC has some weaknesses,such as balancing exploration and *** improve the exploration process within the ABC algorithm,the mean artificial bee colony(MeanABC)by its modified search equation that depends on solutions of mean previous and global best is ***,to solve the main issues of FCM,Automatic clustering algorithm was proposed based on the mean artificial bee colony called(AC-MeanABC).It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value.A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of *** experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.