Abstract
Data mining is mostly utilized for a huge variety of applications in several fields like education, medical, surveillance, and industries. The clustering is an important method of data mining, in which data elements are divided into groups (clusters) to provide better quality data analysis. The Biogeography-Based Optimization (BO) is the latest metaheuristic approach, which is applied to resolve several complex optimization problems. Here, a Chaotic Biogeography-Based Optimization approach using Information Entropy (CBO-IE) is implemented to perform clustering over healthcare IoT datasets. The main objective of CBO-IE is to provide proficient and precise data point distribution in datasets by using Information Entropy concepts and to initialize the population by using chaos theory. Both Information Entropy and chaos theory are facilitated to improve the convergence speed of BO in global search area for selecting the cluster heads and cluster members more accurately. The CBO-IE is implemented to a MATLAB 2021a tool over eight healthcare IoT datasets, and the results illustrate the superior performance of CBO-IE based on F-Measure, intracluster distance, running time complexity, purity index, statistical analysis, root mean square error, accuracy, and standard deviation as compared to previous techniques of clustering like K-Means, GA, PSO, ALO, and BO approaches.
Highlights
Data mining is mostly utilized for a huge variety of applications in several fields like education, medical, surveillance, and industries. e clustering is an important method of data mining, in which data elements are divided into groups to provide better quality data analysis. e Biogeography-Based Optimization (BO) is the latest metaheuristic approach, which is applied to resolve several complex optimization problems
A Chaotic Biogeography-Based Optimization approach using Information Entropy (CBO-IE) is implemented to perform clustering over healthcare IoT datasets. e main objective of CBO-IE is to provide proficient and precise data point distribution in datasets by using Information Entropy concepts and to initialize the population by using chaos theory. Both Information Entropy and chaos theory are facilitated to improve the convergence speed of BO in global search area for selecting the cluster heads and cluster members more accurately. e CBO-IE is implemented to a MATLAB 2021a tool over eight healthcare IoT datasets, and the results illustrate the superior performance of CBO-IE based on FMeasure, intracluster distance, running time complexity, purity index, statistical analysis, root mean square error, accuracy, and standard deviation as compared to previous techniques of clustering like K-Means, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Lion Optimization (ALO), and BO approaches
Introduction e big data [1, 2] is used and analyzed in several wireless applications by utilizing various characteristics like storage, processing, and maintenance of data. e preprocessing of a huge amount of data is performed before analysis to reduce the data redundancy with enhancing the data accuracy and efficiency [3,4,5]. e big data is processed by using various nature inspired optimization approaches like Genetic Algorithm (GA), Ant Colony Optimization (ACO), Ant Lion Optimization (ALO), and Particle Swarm Optimization (PSO) to perform optimal analysis [6]
Summary
CBO-IE: A Data Mining Approach for Healthcare IoT Dataset Using Chaotic Biogeography-Based Optimization and Information Entropy. A Chaotic Biogeography-Based Optimization approach using Information Entropy (CBO-IE) is implemented to perform clustering over healthcare IoT datasets. (3) e CBO-IE is applied over eight healthcare IoT datasets and the outputs are evaluated on the basis of F-Measure, intracluster distance, running time complexity, purity index, statistical analysis, standard deviation, root mean square error, and accuracy as compared to previous techniques of clustering like K-Means, GA, PSO, ALO, and BO approaches. Various researchers mainly solved the issues of infinite size and perception development, but the perception flow and characteristic assessment are not taken into consideration by researchers These two issues are removed in implementing a classification method over big data and parameters like error rate and running time are obtained to provide superior efficiency of the method [34]. Where Tmaximum is the highest mutation rate term illustrated by user and Smaximum is the highest possibility of castes count
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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 call
