An incremental clustering using bat-spotted hyena optimiser with spark framework

作     者：Vidyadhari, Ch. Sandhya, N. Ramakrishnaiah, N. 

作者机构：IT Department Department of Computer Science and Engineering JNTUK Kakinada India VNR Vignana Jyothi Institute of Engineering and Technology Telangana 500090 India CSE Department University College of Engineering JNTUK Kakinada India 

出 版 物：《International Journal of Intelligent Information and Database Systems》 (Int. J. Intell. Inf. Database Syst.)

年 卷 期：2023年第16卷第2期

页      面：167-195页

核心收录：

学科分类：1205[管理学-图书情报与档案管理] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Clustering algorithms 

摘      要：Recently, clustering techniques gained more importance due to huge range of applications in the field of data mining, pattern recognition, data clustering, bio informatics and many other applications. In this paper, a new approach called spotted hyena bat algorithm (SHBA)-based incremental clustering with spark framework is proposed. The SHBA algorithm is derived by integrating the spotted hyena optimiser (SHO) and bat algorithm (BA), that is highly desirable for handling high dimensional data and provides a unique solution with high satisfactory results. The process of incremental clustering is performed in a spark framework by considering the master and the slave nodes. The proposed approach effectively clusters the data, especially high dimensional data and is more robust against various attacks and provides more unified solution. Moreover, the proposed SHBA achieves higher performance by considering the evaluation metrics, such as Jaccard coefficient, rand coefficient, and clustering accuracy of 0.950, 0.943, and 0.962. Copyright © 2023 Inderscience Enterprises Ltd.