Abstract
In this paper, we propose a novel Kernelized Scalable Random Sampling with Iterative Optimization Fuzzy c-Means (KSRSIO-FCM) and a Kernelized Scalable Literal Fuzzy c-Means (KSLFCM) clustering algorithms for big data framework. The evolution of kernelized clustering algorithms led us to deal with the nonlinear separable problems by applying kernel Radial Basis Functions (RBF) which map the input data space nonlinearly into a high-dimensional feature space. The experimental result shows that the KSRSIO-FCM algorithm achieves significant improvement in terms of F-score, Adjusted Rand Index (ARI), and Normalized Mutual Information (NMI) for Big Data. Experimentation is performed on well-known IRIS datasets to show the effectiveness of proposed KSRSIO-FCM in comparison with KSLFCM. The KSRSIO-FCM implemented on Apache Spark shows better potential for Big Data clustering.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
