Research Article CBO-IE: A Data Mining Approach for Healthcare IoT Dataset Using Chaotic Biogeography-Based Optimization and Information Entropy Manish Kumar Ahirwar , 1 Piyush Kumar Shukla , 1 and Rakesh Singhai 2 1 Department of Computer Science and Engineering, UIT-RGPV, Bhopal 462033, India 2 University Institute of Technology RGPV, Shivpuri, M.P. 473551, India Correspondence should be addressed to Piyush Kumar Shukla; pphdwss@gmail.com Received 24 July 2021; Accepted 22 September 2021; Published 8 October 2021 Academic Editor: Punit Gupta Copyright © 2021 Manish Kumar Ahirwar et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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 (clusters) 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. Here, 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 IoTdatasets, 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. 1.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 [3–5]. e big data is processed by using various nature inspired optimization approaches like Genetic Al- gorithm (GA), Ant Colony Optimization (ACO), Ant Lion Optimization (ALO), and Particle Swarm Optimization (PSO) to perform optimal analysis [6]. e data mining [7–9] is a systematic procedure utilized for extracting the secret knowledge and model from an immense, multifaceted, and multidimensional dataset [10]. e association rule mining is one of the famous data mining approaches introduced with MapReduce concept to evaluate the relationship among data elements in a huge dataset [11]. ese data relationships are recognized to generate maxi- mum profit from marketing through IoT devices in in- dustries. e time and security are crucial issues in business, which are frequently resolved by using data mining [12–14]. e data clustering [15] is a type of data mining, in which data components are divided into various sets or groups. e data are collected from various heterogeneous resources; after that time series clustering is applied on this huge amount of data to improve the data accessibility. e in- dustry data are distributed more precisely and accurately by clustering to predict the future aspects of market [16]. e cybercrime data are analyzed after preprocessing to perform training and testing of clustering techniques. e nature of crime is easily understood and detected by clustering the Hindawi Scientific Programming Volume 2021, Article ID 8715668, 14 pages https://doi.org/10.1155/2021/8715668